AI Content On Linkedin

AI Content On Linkedin — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Nona-binning

  Nona-binning is a pixel binning technique used in high-resolution image sensors, primarily in smartphone cameras. The method is based on merging groups of nine neighbouring pixels arranged in a 3×3 pattern. This configuration allows a sensor with very small individual pixels to increase its effective light sensitivity when operating in low-light conditions, while still maintaining high nominal resolution in bright environments. == Overview == Nona-binning is most commonly implemented in sensors with a resolution of 108 megapixels and higher. As pixel counts grew, the physical dimensions of individual pixels continued to shrink, reducing the amount of light captured by each. The 3×3 binning structure enables a sensor to operate in two modes. In well-lit scenes, each pixel is processed separately, providing the full resolution of the sensor. In darker settings, nine pixels with identical colour filters are combined into a single output unit, increasing signal strength and reducing noise. == Technical principles == Unlike the traditional Bayer colour filter array, which alternates colours on a per-pixel basis, nona-binning uses a grouped layout. The sensor forms blocks of nine pixels with matching colour filters — typically within a Quad Bayer–derived arrangement extended to 3×3 regions. When operating in the binning mode, the sensor aggregates the charge generated by all nine pixels in each block. This increases effective sensitivity but lowers the final image resolution. When lighting conditions allow, the sensor returns to processing pixel data individually. == Applications == Nona-binning is primarily used in: Smartphone photography, particularly in devices equipped with sensors exceeding 100 megapixels. Low-light imaging, where increased sensitivity improves exposure stability and reduces noise. Computational photography systems, such as multi-frame processing and HDR capture. == Related technologies == Nona-binning belongs to the broader group of pixel-binning approaches used in modern sensors. Other implementations include Tetracell, which merges four pixels in a 2×2 block, and hexa-binning, which combines six pixels, though it is less common. All of these methods aim to balance the high nominal resolution of mobile sensors with the need for improved low-light performance.

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  Blinding (cryptography)

  In cryptography, blinding first became known in the context of blind signatures, where the message author blinds the message with a random blinding factor, the signer then signs it and the message author "unblinds" it; signer and message author are different parties. Since the late 1990s, blinding mostly refers to countermeasures against side-channel attacks on encryption devices, where the random blinding and the "unblinding" happen on the encryption devices. The techniques used for blinding signatures were adapted to prevent attackers from knowing the input to the modular exponentiation function for Diffie-Hellman or RSA. Blinding must be applied with care, for example Rabin–Williams signatures. If blinding is applied to the formatted message but the random value does not honor Jacobi requirements on p and q, then it could lead to private key recovery. A demonstration of the recovery can be seen in CVE-2015-2141 discovered by Evgeny Sidorov. Side-channel attacks allow an adversary to recover information about the input to a cryptographic operation within an asymmetric encryption scheme, by measuring something other than the algorithm's result, e.g., power consumption, computation time, or radio-frequency emanations by a device. Typically these attacks depend on the attacker knowing the characteristics of the algorithm, as well as (some) inputs. In this setting, blinding serves to alter the algorithm's input into some unpredictable state. Depending on the characteristics of the blinding function, this can prevent some or all leakage of useful information. Note that security depends also on the resistance of the blinding functions themselves to side-channel attacks. == Examples == In RSA blinding involves computing the blinding operation E(x) = (xr)e mod N, where r is a random integer between 1 and N and relatively prime to N (i.e. gcd(r, N) = 1), x is the plaintext, e is the public RSA exponent and N is the RSA modulus. As usual, the decryption function f(z) = zd mod N is applied thus giving f(E(x)) = (xr)ed mod N = xr mod N. Finally it is unblinded using the function D(z) = zr−1 mod N. Multiplying xr mod N by r−1 mod N yields x, as desired. When decrypting in this manner, an adversary who is able to measure time taken by this operation would not be able to make use of this information (by applying timing attacks RSA is known to be vulnerable to) as they does not know the constant r and hence has no knowledge of the real input fed to the RSA primitives. Blinding in GPG 1.x

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  CANaerospace

  CANaerospace is a higher layer protocol based on Controller Area Network (CAN) which has been developed by Stock Flight Systems in 1998 for aeronautical applications. == Background == CANaerospace supports airborne systems employing the Line-replaceable unit (LRU) concept to share data across CAN and ensures interoperability between CAN LRUs by defining CAN physical layer characteristics, network layers, communication mechanisms, data types and aeronautical axis systems. CANaerospace is an open source project, was initiated to standardize the interface between CAN LRUs on the system level. CANaerospace is continuously being developed further and has also been published by NASA as the Advanced General Aviation Transport Experiments Databus Standard in 2001. It found widespread use in aeronautical research worldwide. A major research aircraft that employs several CANaerospace networks for real-time computer interconnection is the Stratospheric Observatory for Infrared Astronomy (SOFIA), a Boeing 747SP with a 2.5m astronomic telescope. CANaerospace is also frequently used in flight simulation and connects entire aircraft cockpits (i.e. in Eurofighter Typhoon simulators) to the simulation host computers. In Italy CANaerospace is used as UAV data bus technology. Furthermore, CANaerospace serves as communication network in several general aviation avionics systems. The CANaerospace interface definition closes the gap between the ISO/OSI layer 1 and 2 CAN protocol (which is implemented in the CAN controller itself) and the specific requirements of distributed systems in aircraft. It may be used as a primary or ancillary avionics network and was designed to meet the following requirements: Democratic network: CANaerospace does not require any master/slave relationships between LRUs or a "bus controller", thereby avoiding a potential single source of failure. Every node in the network has the same rights for participation in the bus traffic. Self-identifying message format: Each CANaerospace message contains information about the type of the data and the transmitting node. This allows the data to be unambiguously recognized at each receiving node. Continuous Message Numbering: Each CANaerospace message contains a continuously incremented number which allows coherent processing of messages in the receiving stations. Message Status Code: Each CANaerospace message contains information about the integrity of the data is conveying. This allows receiving stations to evaluate the quality of the received data and to react accordingly. Emergency Event Signaling: CANaerospace defines a mechanism that allows each node to transmit information about exception or error situations. This information can be used by other stations to determine the network health. Node Service Interface: As an enhancement to CAN, CANaerospace provides a means for individual stations on the network to communicate with each other using connection-oriented and connectionless services. Predefined CAN Identifier Assignment: CANaerospace offers a predefined identifier assignment list for normal operation data. In addition to the predefined list, user-defined identifier assignment lists may be used. Ease of Implementation: The amount of code to implement CANaerospace is very little by design in order to minimize the effort for testing and certification of flight safety critical systems. Openness to Extensions: All CANaerospace definitions are extendable to provide flexibility for future enhancements and to allow adaptions to the requirements of specific applications. Free Availability: No cost whatsoever apply for the use of CANaerospace. The specification can be downloaded from the Internet == Physical interface == To ensure interoperability and reliable communication, CANaerospace specifies the electrical characteristics, bus transceiver requirements and data rates with the corresponding tolerances based on ISO 11898. The bit timing calculation (baud rate accuracy, sample point definition) and robustness to electromagnetic interference are given special emphasis. Also addressed are CAN connector, wiring considerations and design guidelines to maximize electromagnetic compatibility. == Communication layers == The Bosch CAN specification itself allows messages being transmitted both periodically and aperiodically but does not cover issues like data representation, node addressing or connection-oriented protocols. CAN is entirely based on Anyone-to-Many (ATM) communication which means that CAN messages are always received by all stations in the network. The advantage of the CAN concept is inherent data consistency between all stations, the drawback is that it does not allow node addressing which is the basis for Peer-to-Peer (PTP) communication. Using CAN networks in aeronautical applications, however, demands a standard targeted to the specific requirements of airborne systems which implies that communication between individual stations in the network must be possible to enable the required degree of system monitoring. Consequently, CANaerospace defines additional ISO/OSI layer 3, 4 and 6 functions to support node addressing and unified ATM/PTP communication mechanisms. PTP communication allows to set up client/server interactions between individual stations in the network either temporarily or permanently. More than one of these interactions may be in effect at any given time and each node may be client for one operation and server for another at the same time. This CANaerospace mechanism is called "Node Service Concept" and allows i.e. to distribute system functions over several stations in the network or to control dynamic system reconfiguration in case of failure. The Node Service concept supports both connection-oriented and connectionless interactions like with TCP/IP and UDP/IP for Ethernet. Enabling both ATM and PTP communication for CAN requires the introduction of independent network layers to isolate the different types of communication. This is realized for CANaerospace by forming CAN identifier groups as shown in Figure 1. The resulting structure creates Logical Communication Channels (LCCs) and assigns a specific communication type (ATM, PTP) to each of the LCCs. User-defined LCCs provide the necessary freedom for designers and allow the implementation of CANaerospace according to the needs of specific applications. Figure 1: Logical Communication Channels for CANaerospace As a side effect, the CAN identifier groups in Figure 1 affect the priority of the message transmission in case of bus arbitration. The communication channels are therefore arranged according to their relative importance: Emergency Event Data Channel (EED): This communication channel is used for messages which require immediate action (i.e. system degradation or reconfiguration) and have to be transmitted with very high priority. Emergency Event Data uses ATM communication exclusively. High/Low Priority Node Service Data Channel (NSH/NSL): These communication channels are used for client/server interactions using PTP communication. The corresponding services may be of the connection-oriented as well as the connectionless type. NSH/NSL may also be used to support test and maintenance functions. Normal Operation Data Channel (NOD): This communication channel is used for the transmission of the data which is generated during normal system operation and described in the CANaerospace identifier assignment list. These messages may be transmitted periodically or aperiodically as well as synchronously or asynchronously. All messages which cannot be assigned to other communication channels shall use this channel. High/Low Priority User-Defined Data Channel (UDH/UDL): This channel is dedicated to communication which cannot, due to their specific characteristics, be assigned other channels without violating the CANaerospace specification. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. To ensure interoperability it is highly recommended that the use of these channels is minimized. Debug Service Data Channel (DSD): This channel is dedicated to messages which are used temporarily for development and test purposes only and are not transmitted during normal operation. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. == Data representation == The majority of the real-time control systems used in aeronautics employ "big endian" processor architectures. This data representation was therefore specified for CANaerospace as well. With big endian data representation, the most significant bit of any datum is arranged leftmost and transmitted first on CANaerospace as shown in Figure 2. Figure 2: "Big Endian" Data Representation for CANaerospace CANaerospace uses a self-identifying message

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  ESign (India)

  Aadhaar eSign is an online electronic signature service in India to facilitate an Aadhaar holder to digitally sign a document. The signature service is facilitated by authenticating the Aadhaar holder via the Aadhaar-based e-KYC (electronic Know Your Customer) service. To eSign a document, one has to have an Aadhaar card and a mobile number registered with Aadhaar. With these two things, an Indian citizen can sign a document remotely without being physically present. == Procedure == The notification issued by Government of India in this regard stipulates the following procedure for the e-authentication using Aadhaar e-KYC services. Authentication of an electronic record by e-authentication technique, which shall be done by the applicable use of e-authentication, hash function, and asymmetric cryptosystem techniques, leading to issuance of digital signature certificate by Certifying Authority, a trusted third party service by subscriber's key pair generation, storing of the key pairs on hardware security module and creation of digital signature provided that the trusted third party shall be offered by the certifying authority (the trusted third party shall send application form and certificate signing request to the Certifying Authority for issuing a digital signature certificate to the subscriber), issuance of digital signature certificate by Certifying Authority shall be based on e-authentication, particulars given in the prescribed format, digitally signed verified information from Aadhaar e-KYC services and electronic consent of digital signature certificate applicant, the manner and requirements for e-authentication shall be as issued by the Controller from time to time, the security procedure for creating the subscriber's key pair shall be in accordance with the e-authentication guidelines issued by the Controller, the standards referred to in rule 6 of the Information Technology (Certifying Authorities) Rules, 2000 shall be complied with, in so far as they relate to the certification function of public key of Digital Signature Certificate applicant, and the manner in which information is authenticated by means of digital signature shall comply with the standards specified in rule 6 of the Information Technology (Certifying Authorities) Rules, 2000 in so far as they relate to the creation, storage and transmission of Digital Signature. == eSign Service Providers == Organisations and individuals seeking to obtain the eSigning Service can utilize the services of various service providers. There are empanelled service providers with whom organisations can register as an Application Service Prover after submitting the requisite documents, getting UAT access, building the application around the service and going through an IT Audit by an CERT-IN empanelled auditor. However, the process of registering as an Application Service Provider is cumbersome, and requires huge investments of time, money and resources in complying with the regulations and building a suitable application. Most organisations prefer using services of plug-n-play gateway providers who take the responsibility of complying with the regulations, hence simplifying the process for the market.

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  IQTELL

  IQTELL was a productivity app that allowed users to manage email, tasks, projects, calendars, contacts, Evernotes and more in a single app. IQTELL was available as a web app, as well as an iOS and Android app. All user information was automatically synced between all devices. iOS and Android apps supported offline access. The app could be used to implement concepts and techniques described in the book Getting Things Done by David Allen. == History == IQTELL was created by Ran Flam and released in 2013. In 2014, mobile apps for iOS and Android were released. In 2015, Premium and Platinum subscription plans were introduced (while maintaining the free user version). In April 2017, a new web app was launched. On July 31, 2017, all IQTell services have been closed. == Productivity methods == IQTell was designed to fit in with the Getting Things Done (GTD) productivity methods. Users may have had utilized GTD lists, such as Inbox, Actions, Projects, Someday, Ticklers, and Reference information to process their Inbox items into relevant GTD lists. Using the web app, iOS and/or Android apps, users could deploy macros/shortcuts to quickly process their email. Email was turned into tasks (actions), projects, etc. The original email was removed from the email inbox. The email became a part of the items created (e.g. actions, project, etc.) and could also be viewed in the All Mail folder (if Gmail), or the Archive folder (if non-Gmail). Users had flexibility to use the out-of-the-box macros/shortcuts as well as edit/create additional macros. IQTELL features included email, calendars, contacts, list management, sharing and collaboration with team members. All of the features were compatible with commonly used organization software such as Evernote and iCloud.

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  Tropical cryptography

  In tropical analysis, tropical cryptography refers to the study of a class of cryptographic protocols built upon tropical algebras. In many cases, tropical cryptographic schemes have arisen from adapting classical (non-tropical) schemes to instead rely on tropical algebras. The case for the use of tropical algebras in cryptography rests on at least two key features of tropical mathematics: in the tropical world, there is no classical multiplication (a computationally expensive operation), and the problem of solving systems of tropical polynomial equations has been shown to be NP-hard. == Basic Definitions == The key mathematical object at the heart of tropical cryptography is the tropical semiring ( R ∪ { ∞ } , ⊕ , ⊗ ) {\displaystyle (\mathbb {R} \cup \{\infty \},\oplus ,\otimes )} (also known as the min-plus algebra), or a generalization thereof. The operations are defined as follows for x , y ∈ R ∪ { ∞ } {\displaystyle x,y\in \mathbb {R} \cup \{\infty \}} : x ⊕ y = min { x , y } {\displaystyle x\oplus y=\min\{x,y\}} x ⊗ y = x + y {\displaystyle x\otimes y=x+y} It is easily verified that with ∞ {\displaystyle \infty } as the additive identity, these binary operations on R ∪ { ∞ } {\displaystyle \mathbb {R} \cup \{\infty \}} form a semiring.

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  Ciphertext

  In cryptography, ciphertext or cyphertext is the result of encryption performed on plaintext using an algorithm, called a cipher. Ciphertext is also known as encrypted or encoded information because it contains a form of the original plaintext that is unreadable by a human or computer without the proper cipher to decrypt it. This process prevents the loss of sensitive information via hacking. Decryption, the inverse of encryption, is the process of turning ciphertext into readable plaintext. Ciphertext is not to be confused with codetext, because the latter is a result of a code, not a cipher. == Conceptual underpinnings == Let m {\displaystyle m\!} be the plaintext message that Alice wants to secretly transmit to Bob and let E k {\displaystyle E_{k}\!} be the encryption cipher, where k {\displaystyle _{k}\!} is a cryptographic key. Alice must first transform the plaintext into ciphertext, c {\displaystyle c\!} , in order to securely send the message to Bob, as follows: c = E k ( m ) . {\displaystyle c=E_{k}(m).\!} In a symmetric-key system, Bob knows Alice's encryption key. Once the message is encrypted, Alice can safely transmit it to Bob (assuming no one else knows the key). In order to read Alice's message, Bob must decrypt the ciphertext using E k − 1 {\displaystyle {E_{k}}^{-1}\!} which is known as the decryption cipher, D k : {\displaystyle D_{k}:\!} D k ( c ) = D k ( E k ( m ) ) = m . {\displaystyle D_{k}(c)=D_{k}(E_{k}(m))=m.\!} Alternatively, in a non-symmetric key system, everyone, not just Alice and Bob, knows the encryption key; but the decryption key cannot be inferred from the encryption key. Only Bob knows the decryption key D k , {\displaystyle D_{k},} and decryption proceeds as D k ( c ) = m . {\displaystyle D_{k}(c)=m.} == Types of ciphers == The history of cryptography began thousands of years ago. Cryptography uses a variety of different types of encryption. Earlier algorithms were performed by hand and are substantially different from modern algorithms, which are generally executed by a machine. === Historical ciphers === Historical pen and paper ciphers used in the past are sometimes known as classical ciphers. They include: Substitution cipher: the units of plaintext are replaced with ciphertext (e.g., Caesar cipher and one-time pad) Polyalphabetic substitution cipher: a substitution cipher using multiple substitution alphabets (e.g., Vigenère cipher and Enigma machine) Polygraphic substitution cipher: the unit of substitution is a sequence of two or more letters rather than just one (e.g., Playfair cipher) Transposition cipher: the ciphertext is a permutation of the plaintext (e.g., rail fence cipher) Historical ciphers are not generally used as a standalone encryption technique because they are quite easy to crack. Many of the classical ciphers, with the exception of the one-time pad, can be cracked using brute force. === Modern ciphers === Modern ciphers are more secure than classical ciphers and are designed to withstand a wide range of attacks. An attacker should not be able to find the key used in a modern cipher, even if they know any specifics about the plaintext and its corresponding ciphertext. Modern encryption methods can be divided into the following categories: Private-key cryptography (symmetric key algorithm): one shared key is used for encryption and decryption Public-key cryptography (asymmetric key algorithm): two different keys are used for encryption and decryption In a symmetric key algorithm (e.g., DES, AES), the sender and receiver have a shared key established in advance: the sender uses the shared key to perform encryption; the receiver uses the shared key to perform decryption. Symmetric key algorithms can either be block ciphers or stream ciphers. Block ciphers operate on fixed-length groups of bits, called blocks, with an unvarying transformation. Stream ciphers encrypt plaintext digits one at a time on a continuous stream of data, with the transformation of successive digits varying during the encryption process. In an asymmetric key algorithm (e.g., RSA), there are two different keys: a public key and a private key. The public key is published, thereby allowing any sender to perform encryption. The private key is kept secret by the receiver, thereby allowing only the receiver to correctly perform decryption. == Cryptanalysis == Cryptanalysis (also referred to as codebreaking or cracking the code) is the study of applying various methodologies to obtain the meaning of encrypted information, without having access to the cipher required to correctly decrypt the information. This typically involves gaining an understanding of the system design and determining the cipher. Cryptanalysts can follow one or more attack models to crack a cipher, depending upon what information is available and the type of cipher being analyzed. Ciphertext is generally the most easily obtained part of a cryptosystem and therefore is an important part of cryptanalysis. === Attack models === Ciphertext-only: the cryptanalyst has access only to a collection of ciphertexts or code texts. This is the weakest attack model because the cryptanalyst has limited information. Modern ciphers rarely fail under this attack. Known-plaintext: the attacker has a set of ciphertexts to which they know the corresponding plaintext Chosen-plaintext attack: the attacker can obtain the ciphertexts corresponding to an arbitrary set of plaintexts of their own choosing Batch chosen-plaintext attack: where the cryptanalyst chooses all plaintexts before any of them are encrypted. This is often the meaning of an unqualified use of "chosen-plaintext attack". Adaptive chosen-plaintext attack: where the cryptanalyst makes a series of interactive queries, choosing subsequent plaintexts based on the information from the previous encryptions. Chosen-ciphertext attack: the attacker can obtain the plaintexts corresponding to an arbitrary set of ciphertexts of their own choosing Adaptive chosen-ciphertext attack Indifferent chosen-ciphertext attack Related-key attack: similar to a chosen-plaintext attack, except the attacker can obtain ciphertexts encrypted under two different keys. The keys are unknown, but the relationship between them is known (e.g., two keys that differ in the one bit). == Famous ciphertexts == The Babington Plot ciphers The Shugborough inscription The Zimmermann Telegram The Magic Words are Squeamish Ossifrage The cryptogram in "The Gold-Bug" Beale ciphers Kryptos Zodiac Killer ciphers

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  Data refuge

  Data Refuge is a public and collaborative project designed to address concerns about federal climate and environmental data that is in danger of being lost. In particular, the initiative addresses five main concerns: What are the best ways to safeguard data? How do federal agencies play a crucial role in collecting, managing, and distributing data? How do government priorities impact data's accessibility? Which projects and research fields depend on federal data? Which data sets are of value to research and local communities, and why? Data Refuge began as a grassroots organization in opposition to government data on climate change and the environment not being archived systemically. Data Refuge's main goal is to collect and allocate data in multiple safe locations to create a sustainable way of archiving old and new data. Data Refuge was initiated in 2016 to protect federal climate and environmental data that is vulnerable under an administration that denies climate change. The system aims to make public research-quality copies of federal climate and environmental data. Data Refuge is supported by the National Geographic Foundation, private donors, Libraries+ Network, Preserving Electronic Governance Initiative (PEGI), the Union of Concerned Scientists (USC), and the Penn Program in Environmental Humanities (PPEH). == Types of data == Data Refuge collects public federal data on the climate and environment in the form of satellite imagery, PDFs, and stories. The data are stored in multiple trusted locations as they are less vulnerable if in only one location, and to ensure accessibility for researchers. Through the Data Rescue events, Data Refuge has accumulated 4 terabytes of data, 30,000 URLs, and 800 participants. === Storytelling === Data Refuge collects stories on vulnerable federal climate and environmental data through: surveys, oral history, photo essays, maps, video shorts, and animations. The stories are archived in a public bank that showcase how federal environmental data support health and safety in communities. Data Stories are collected at Data Rescue events, which are partnered with universities, city and town halls, and advocacy groups. Data stories are collected and used to emphasize the importance of Data Refuge, in how the data on climate change and the environment are being used by people in the United States and across the world for meaningful practices.

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  Tandem (app)

  Tandem is a mobile language exchange and language learning app. == History == Tandem was founded in Hannover, Germany in 2014 by Arnd Aschentrup, Tobias Dickmeis, and Matthias Kleimann. Prior to founding Tandem, the trio had launched Vive, a members-only mobile video chat platform. Tandem has been criticised for not accepting members into the community immediately, as opposed to competitors including HelloTalk, Speaky or Cafehub. In some countries, there is a waiting list and applicants can wait up to seven days for their application to be processed by human moderators. In 2015, Tandem completed its first funding round (seed funding) of €600,000. Participating investors included business angels such as Atlantic Labs (Christophe Maire), Hannover Beteiligungsfonds, Marcus Englert (Chairman of the Supervisory Board of Rocket Internet SE ), Catagonia, Ludwig zu Salm, Florian Langenscheidt, Heiko Hubertz, Martin Sinner, and Zehden Enterprises. In 2016, the company received a further €2 million from new investors Rubylight and Faber Ventures, as well as from existing investors Hannover Beteiligungsfonds, Atlantic Labs, and Zehden Enterprises. Since 2018, the premium membership Tandem Pro has been available, which offers members unlimited access to all language learning features of the app as well as the removal of advertising for a monthly fee.

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  Data custodian

  In data governance groups, responsibilities for data management are increasingly divided between the business process owners and information technology (IT) departments. Two functional titles commonly used for these roles are data steward and data custodian. Data Stewards are commonly responsible for data content, context, and associated business rules. Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler, ETL developer and data engineer. == Data custodian responsibilities == A data custodian ensures: Access to the data is authorized and controlled Data stewards are identified for each data set Technical processes sustain data integrity Processes exist for data quality issue resolution in partnership with data stewards Technical controls safeguard data Data added to data sets are consistent with the common data model Versions of master data are maintained along with the history of changes Change management practices are applied in maintenance of the database Data content and changes can be audited

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  Data room

  Data rooms are secure spaces used for housing data, usually of a privileged or confidential nature. They can be physical data rooms, virtual data rooms (VDRs), or data centers. They are primarily used for a variety of corporate purposes, including data storage, document exchange, file sharing, financial transactions, and legal proceedings. Today, data rooms are central to workflows in mergers and acquisitions, venture capital, and corporate restructuring, increasingly utilizing artificial intelligence to securely manage and review large datasets. Historically, data rooms were strictly physical locations heavily guarded and monitored. Today, the vast majority of corporate data rooms are hosted virtually on secure cloud platforms, though physical rooms are still occasionally used for highly sensitive government or proprietary intelligence. == Physical Data Rooms == In mergers and acquisitions (M&A), the traditional data room genuinely consists of a physically secured and continually monitored room, normally in the vendor's offices or those of their legal counsel. Bidders and their advisers visit this room in order to inspect and report on various documents, legal contracts, and financial statements made available during the due diligence process. Historically, physical data rooms presented significant logistical challenges. Often, only one bidder at a time was allowed to enter to maintain document integrity and confidentiality. If new documents or new versions of documents were required, they had to be brought in by courier as hardcopies. Teams involved in large due diligence processes typically had to be flown in from many regions or countries and remain available throughout the process. Because these teams comprised a number of experts in different fields—such as legal counsel, forensic accountants, and industry specialists—the overall cost of keeping such groups on call near the physical data room was often extremely high. == Virtual Data Rooms (VDRs) == To address the costs and logistical bottlenecks of physical data rooms, virtual data rooms (VDRs) were developed to provide secure, online dissemination of confidential information. A VDR is essentially a secure cloud repository with strictly controlled access. Access is managed through secure log-ons supplied by the vendor or authority, which can be disabled at any time if a bidder withdraws from a transaction. Because much of the information released during corporate transactions is highly confidential, VDRs utilize digital rights management (DRM) to control information. Restrictions are applied to the viewers' ability to release data to third parties by disabling forwarding, copying, or printing capabilities. Modern VDRs also employ dynamic watermarking and detailed auditing capabilities. Detailed auditing is required for legal reasons so that a precise digital footprint is kept of who has viewed which version of each document, and for how long. Furthermore, modern VDR platforms are typically built to comply with stringent information security standards such as ISO 27001 and SOC 2. Transitioning from sequential physical data rooms to parallel virtual data rooms has been shown to significantly reduce the duration of M&A transactions while allowing sellers to field multiple bidders simultaneously. == Key Applications == Data rooms are commonly used by legal, accounting, investment banking, and private equity firms. Primary applications include: Mergers and Acquisitions (M&A): VDRs are central to the sell-side M&A process. After potential buyers sign a Non-Disclosure Agreement (NDA) and review a Confidential Information Memorandum (CIM), they are granted data room access to perform deep financial due diligence, such as Quality of Earnings (QoE) analysis and legal liability assessments. Venture Capital and Startups: Startups use data rooms as a centralized location for key operational data, capitalization tables, and financial projections to streamline due diligence for angel investors and venture capital firms during fundraising rounds. Initial Public Offerings (IPOs): Taking a company public requires intense regulatory scrutiny. Data rooms are used to securely share company histories and financial audits with investment bankers, legal teams, and regulatory bodies. Corporate Restructuring and Insolvency: During bankruptcies or corporate carve-outs, data rooms are used to organize outstanding debt profiles, creditor agreements, and operational liabilities. == Emerging Technologies == In recent years, the management of virtual data rooms has increasingly incorporated Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Natural Language Processing (NLP) tools are now integrated into VDRs to automatically index thousands of documents, perform auto-redaction of personally identifiable information (PII), and assist buy-side analysts in identifying hidden liabilities within unstructured text data during the due diligence phase. Modern AI algorithms can extract line items from financial statements to instantly populate structured databases.

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  Data marketplace

  Data marketplace is an online platform for sharing and consuming data in the form of data assets or data products. Part of the data management stack, it aims to bring together data producers and data consumers (including business users and AI) in a single space, with the objective of increasing access to understandable, high-quality data. Included within its Data Marketplaces and Exchange (DME) category by Gartner, data marketplaces can provide data internally within an organization, externally with partners, or as open data. == Concept == Digitization has dramatically increased data volumes within organizations, with IDC predicting that by 2025 the world will contain 175 zettabytes of data. This has created a need to both manage this data and provide access to it to enable business intelligence and data analysis. However, data is often scattered within multiple systems (such as data warehouses and data lakes), and is in formats that are only understandable by technical experts, such as data scientists. According to IDC, 81% of IT leaders cite data silos as a major barrier to digital transformation. This means that data is not freely available to business users or external audiences such as partners or citizens, limiting its value, and holding back AI deployments. Data marketplaces solve this issue, providing seamless, self-service access to high-quality data in an understandable, secure and auditable manner. They break down data silos, reduce friction in data access, and enable a broader range of users, including non-technical profiles, to find, understand, and consume data autonomously. Data assets on the marketplace can be raw data, data visualizations or data products. Data marketplaces combine data management functions such as data governance with the user-friendly experience offered by e-commerce marketplaces in order to increase the usage of data. These include features such as powerful search engines, feedback, ratings, subscriptions and product description sheets. According to Gartner, data marketplaces provide infrastructure, transactional capabilities, and services for both consumers and providers of data assets. == History and timeline == Data marketplaces have evolved since they first emerged in terms of both their scope and usage. === 2000s === With the rise of the internet, data brokers began collecting, aggregating, distributing and selling personal, financial and marketing data to third parties online. Data marketplaces were deployed to monetize this data, making it discoverable and accessible to users, either through subscriptions or one-off purchases. At the same time, regulations, such as the US Open Government Initiative of 2009 and others around the world mandated greater transparency and data sharing with the public. Data sharing portals were created by public and government bodies to make this information available through self-service to all users. === 2010s === Due to the growth of big data and cloud platforms, cloud-based data exchange platforms emerged. These were offered by major infrastructure providers, and included Amazon Web Services (AWS) Data Exchange, Snowflake Data Marketplace, and the Google Cloud Platform. These platforms moved beyond simple data brokerage or open data by providing structured, catalogued data sharing between organizations. === 2020s === Driven by a need to increase internal data sharing with both business users and AI, organizations are now looking to adopt internal data marketplaces. These aim to democratize data consumption by providing seamless access for all employees and AI to trusted data, including data products, through an intuitive, e-commerce style experience. According to Gartner analyst Richa Jha, "by providing a single, governed platform for discovering, sharing, and scaling data products, data marketplaces drive productivity, collaboration, and ROI across the enterprise." == Data marketplaces within the overall data architecture == Data marketplaces provide a consumption and collaboration layer for data. That means they complement and integrate with other parts of the overall data architecture, including: === Data warehouses and data lakes === Data marketplaces connect to data sources, such as data warehouses or data lakes, to provide intuitive access to the data stored within them, enabling data to be shared and distributed to non-technical audiences. Access can be direct, with data and data products stored within the data marketplace or virtualized. === Data catalog === A data catalog provides a technical inventory of an organization's data estate. It collects technical information on all available data assets within an organization, based on metadata descriptions. This ensures traceability, and supports compliance and governance requirements. Unlike a data marketplace, a data catalog does not provide access to data, and is designed to be used by data professionals, rather than the business. This means it lacks an intuitive, understandable interface and is consequently not easily accessible by business users. === Data mesh === Data mesh is an architecture and framework for data management, first defined by Zhamak Dehghani in 2019. It aims to decentralize data ownership to delegate responsibility, empowering teams and focusing on delivering data to users in the form of self-service data products. The data marketplace is a central pillar of data mesh, providing intuitive access to these data products, and creating a collaboration space for data owners and data consumers. === Data product === Data products are high-value, consumable data assets that package high-quality data and associated tools to enable seamless usage by business users at scale. First defined by McKinsey in 2022, they have an identified owner, a service level agreement (SLA), and a reusability logic. == Core components of a data marketplace == A data marketplace typically includes specific core components: === E-commerce style interface === An e-commerce style experience that engages non-technical users, minimizes the need for training and builds confidence and trust in data. Look and feel should be customizable to incorporate corporate design guidelines to ensure consistency with other organizational applications. === Built-in data catalog === As in a standalone data catalog, this indexes all available data, based on metadata that includes type, source, owner, freshness, and quality level. === Discovery and search engine === This enables users to search, filter, explore and discover available data intuitively. As in an e-commerce marketplace, it should be intelligent, and provide relevant results based on natural language queries. === Access control and security management === Data marketplaces will contain data that needs to be protected under regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and sector-specific frameworks in industries such as finance and healthcare. To ensure both security and compliance while maximizing data consumption, the data marketplace should include granular access management and a full audit trail. === Semantic layer and business glossary === Different parts of the business are likely to use different terms to describe data. This leads to inconsistencies and an inability to share data across systems and teams. The semantic layer and business glossary standardize a shared vocabulary and common definitions of business indicators and concepts, providing a single language for data across the business and for AI agents. === Data governance mechanisms === These enforce corporate data governance policies, ensuring data traceability through data lineage, quality certification, usage monitoring, and continuous improvement through user feedback loops. === Collaboration features === As on an e-commerce website, a data marketplace should provide collaboration features that bring together data users and data owners. This includes the ability to rate data products, share use cases, and provide feedback to data owners, creating a community around data and supporting a data-driven culture. == Types of data marketplace == While they share the same underlying technology, data marketplaces can be deployed in three broad ways: === Internal data marketplaces === These bring together data from across an organization and make it available via self-service to employees from across the business. They aim to widen access to data and consequently to improve decision-making and reporting, increase performance and maximize efficiency. === Ecosystem data marketplaces === These extend sharing beyond a single organization, enabling multiple partners (public institutions, industry players, research bodies) to share and consume data within a governed framework. Data can be provided by all parties or simply by one organization and consumed by others. Ecosystem data marketplaces are particularly relevant in

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  AI nationalism

  AI nationalism is the idea that nations should develop and control their own artificial intelligence technologies to advance their own interests and ensure technological sovereignty. This concept is gaining traction globally, leading countries to implement new laws, form strategic alliances, and invest significantly in domestic AI capabilities. == Global trends and national strategies == In 2018, British technology investor Ian Hogarth published an influential essay titled AI Nationalism. He argued that as AI gains more power and its economic and military significance expands, governments will take measures to bolster their own domestic AI industries, and predicted that the advancement of machine learning systems would lead to what he termed "AI nationalism." He anticipated that this rise in AI would accelerate a global arms race, resulting in more closed economies, restrictions on foreign acquisitions, and limitations on the movement of talent. Hogarth predicted that AI policy would become a central focus of government agendas. He also criticized Britain’s approach to AI strategy, citing the sale of London-based DeepMind—one of the leading AI laboratories, acquired by Google for a relatively modest £400 million in 2014—as a significant misstep. AI nationalism is chiefly reflected in the escalating rhetoric of an artificial intelligence arms race, portraying AI development as a zero-sum game where the winner gains significant economic, political, and military advantages. This mindset, as highlighted in a 2017 Pentagon report, warns that sharing AI technology could erode technological supremacy and enhance rivals' capabilities. The winner-takes-all mentality of AI nationalism poses risks including unsafe AI development, increased geopolitical tension, and potential military aggression (such as cyberattacks or targeting AI professionals). Several countries, including Canada, France, and India, have formulated national strategies to advance their positions in AI. In the United States, a leading player in the global AI arena, trade policies have been enacted to restrict China's access to critical microchips, reflecting a strategic effort to maintain a technological edge. The United States’ National Security Commission on Artificial Intelligence (NSCAI) frames AI development as a critical aspect of a broader technology competition crucial for national success. It emphasizes the need to outpace China in AI to maintain strategic advantage, reflecting AI nationalism by linking geopolitical power directly to advancements in AI. France has seen notable governmental support for local AI startups, particularly those specializing in language technologies that cater to French and other non-English languages. In Saudi Arabia, Crown Prince Mohammed bin Salman is investing billions in AI research and development. The country has actively collaborated with major technology firms such as Amazon, IBM, and Microsoft to establish itself as a prominent AI hub. == Historical and cultural context == AI nationalism is seen as deeply connected to historical racism and imperialism. It is viewed not merely as a technological competition but as a contest over racial and civilizational superiority. Historically, technological achievements were often used to justify colonialism and racial hierarchies, with Western societies perceiving their advancements as evidence of superiority. In the context of AI, this historical context continues to shape views on intelligence and development. Some argue that AI nationalism reinforces the idea of fundamental civilizational divides, especially between the Western world and China. This perspective often frames China's progress in AI as a direct challenge to Western values, presenting the AI competition as a struggle over values. AI nationalism is said to draw from long-standing anti-Asian stereotypes, such as the "Yellow Peril," which portray Asian nations as threats to Western civilization. This viewpoint links Asian technological advances with dehumanization and artificiality, reflecting persistent anxieties about China's growing role in the global tech landscape. == Implications == AI nationalism is seen as a component of a broader trend towards the fragmentation of the internet, where digital services are increasingly influenced by local regulations and national interests. This shift is creating a new technological landscape in which the impact of artificial intelligence on individuals' lives can vary significantly depending on their geographic location. J. Paul Goode argues that AI nationalism may exacerbate existing societal divisions by promoting the development of systems that embed cultural biases, thereby privileging certain groups while disadvantaging others.

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  Conjugate coding

  Conjugate coding is a cryptographic tool, introduced by Stephen Wiesner in the late 1960s. It is part of the two applications Wiesner described for quantum coding, along with a method for creating fraud-proof banking notes. The application that the concept was based on was a method of transmitting multiple messages in such a way that reading one destroys the others. This is called quantum multiplexing and it uses photons polarized in conjugate bases as "qubits" to pass information. Conjugate coding also is a simple extension of a random number generator. At the behest of Charles Bennett, Wiesner published the manuscript explaining the basic idea of conjugate coding with a number of examples but it was not embraced because it was significantly ahead of its time. Because its publication has been rejected, it was developed to the world of public-key cryptography in the 1980s as oblivious transfer, first by Michael Rabin and then by Shimon Even. It is used in the field of quantum computing. The initial concept of quantum cryptography developed by Bennett and Gilles Brassard was also based on this concept.

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  Corporate surveillance

  Corporate surveillance describes the practice of businesses monitoring and extracting information from their users, clients, or staff. This information may consist of online browsing history, email correspondence, phone calls, location data, and other private details. Acts of corporate surveillance frequently look to boost results, detect potential security problems, or adjust advertising strategies. These practices have been criticized for violating ethical standards and invading personal privacy. Critics and privacy activists have called for businesses to incorporate rules and transparency surrounding their monitoring methods to ensure they are not misusing their position of authority or breaching regulatory standards. Monitoring can feel intrusive and give the impression that the business does not promote ethical behavior among its personnel. Staff satisfaction, productivity, and staff turnover may all suffer as a result of the invasion of privacy. == Monitoring methods == Employers may be authorized to gather information through keystroke logging and mouse tracking, which involves recording the keys individuals interact with and cursor position on computers. In cases where employment contracts permit it, they may also monitor webcam activity on company-provided computers. Employers may be able to view the emails sent from business accounts and may be able to see the websites visited when using a corporate internet connection. The screenshot capability is another tool that enables companies to see what remote workers are doing. This feature, which can be found in tracking software, takes screenshots throughout the day at predetermined or arbitrary intervals. Additionally, people who don't work in offices are observed. For instance, it has been claimed that Amazon has incorporated tracking technology to monitor warehouse staff and delivery drivers. == Use of collected information == Information collected by corporations can be used for a variety of uses including marketing research, targeting advertising, fraud detection and prevention, ensuring policy adherence, preventing lawsuits, and safeguarding records and company assets. == Privacy concerns == Concerns over corporate privacy have become more important due to companies collection and manipulation of personal data. Since these practices have been recognized there has been a rising concern about both the security and the possible mishandling of the data accumulated. Social Media data collection and monitoring has been one of the most concerned areas regarding corporate surveillance. Recently, many employers on CareerBuilder have checked their potential candidates' social media activities before the hiring process. This approach can be excusable since it is important to be aware of a future employee or applicant's online presence, and how it might affect the company's reputation in the future. This is crucial since employers are often made legally responsible for their worker's digital actions. These data can also be used to enact political gains. The Facebook-Cambridge Analytica data scandal in 2018 revealed that its British branch to have surreptitiously sold American psychological data to the Trump campaign. This information was supposed to be private, but Facebook's inability to protect user information had reportedly not been a top priority of the company at the time. == Laws and regulations == The National Labor and Relations Act (NLRA) safeguards workplace democracy by giving workers in the private sector the basic freedom to demand better working conditions and choice of representation without fear of retaliation. General Data Protection Regulation (GDPR) outlines the broad responsibilities of data controllers and the "processors" that handle personal data on their behalf. They must adopt the necessary security measures in accordance with the risk involved in the data processing operations they carry out.[1] Electronics Communication Privacy Act (ECPA), as amended, provides protection for electronic, oral, and wire communications while they are being created, while they are being sent, and while they are being stored on computers. Email, phone calls, and electronically stored data are covered by the Act. == Sale of customer data == If it is business intelligence, data collected on individuals and groups can be sold to other corporations, so that they can use it for the aforementioned purpose. It can be used for direct marketing purposes, such as targeted advertisements on Google and Yahoo. These ads are tailored to the individual user of the search engine by analyzing their search history and emails (if they use free webmail services). For example, the world's most popular web search engine stores identifying information for each web search. Google stores an IP address and the search phrase used in a database for up to 2 years. Google also scans the content of emails of users of its Gmail webmail service, in order to create targeted advertising based on what people are talking about in their personal email correspondences. Google is, by far, the largest web advertising agency. Their revenue model is based on receiving payments from advertisers for each page-visit resulting from a visitor clicking on a Google AdWords ad, hosted either on a Google service or a third-party website. Millions of sites place Google's advertising banners and links on their websites, in order to share this profit from visitors who click on the ads. Each page containing Google advertisements adds, reads, and modifies cookies on each visitor's computer. These cookies track the user across all of these sites, and gather information about their web surfing habits, keeping track of which sites they visit, and what they do when they are on these sites. This information, along with the information from their email accounts, and search engine histories, is stored by Google to use for building a profile of the user to deliver better-targeted advertising. == Surveillance of workers == In 1993, David Steingard and Dale Fitzgibbons argued that modern management, far from empowering workers, had features of neo-Taylorism, where teamwork perpetuated surveillance and control. They argued that employees had become their own "thought police" and the team gaze was the equivalent of Bentham's panopticon guard tower. A critical evaluation of the Hawthorne Plant experiments has in turn given rise to the notion of a Hawthorne effect, where workers increase their productivity in response to their awareness of being observed or because they are gratified for being chosen to participate in a project. According to the American Management Association and the ePolicy Institute, who undertook a quantitative survey in 2007 about electronic monitoring and surveillance with approximately 300 US companies, "more than one fourth of employers have fired workers for misusing email and nearly one third have fired employees for misusing the Internet." Furthermore, about 30 percent of the companies had also fired employees for usage of "inappropriate or offensive language" and "viewing, downloading, or uploading inappropriate/offensive content." More than 40 percent of the companies monitor email traffic of their workers, and 66 percent of corporations monitor Internet connections. In addition, most companies use software to block websites such as sites with games, social networking, entertainment, shopping, and sports. The American Management Association and the ePolicy Institute also stress that companies track content that is being written about them, for example by monitoring blogs and social media, and scanning all files that are stored in a filesystem. == Government use of corporate surveillance data == The United States government often gains access to corporate databases, either by producing a warrant for it, or by asking. The Department of Homeland Security has openly stated that it uses data collected from consumer credit and direct marketing agencies—such as Google—for augmenting the profiles of individuals that it is monitoring. The US government has gathered information from grocery store discount card programs, which track customers' shopping patterns and store them in databases, in order to look for terrorists by analyzing shoppers' buying patterns. == Corporate surveillance of citizens == According to Dennis Broeders, "Big Brother is joined by big business". He argues that corporations are in any event interested in data on their potential customers and that placing some forms of surveillance in the hands of companies, results in companies owning video surveillance data for stores and public places. The commercial availability of surveillance systems has led to their rapid spread. Therefore it is almost impossible for citizens to maintain their anonymity. When businesses can monitor their customers, such customers run the risk of facing prejudice when applying for housing, loans, jobs, and other economic opportun

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