- Data Flow The movement or transfer of data through a system, from one component or process to another, often visualized in diagrams to represent the path that data takes through software, systems, or networks. Data flow analysis is crucial in designing systems for efficiency and understanding how data is processed and utilized.
- Data Flow Control Mechanisms that regulate the rate at which data is sent or received in a network to avoid overwhelming a recipient or communications channel. This ensures reliable data transfer and efficient use of network resources and can enhance overall system performance. Flow control can be implemented via software at the application, transport, or network layers and is integral to network protocols like TCP.
- Data Flow Diagrams (DFDs) Graphical representations that illustrate the flow of data through an information system. DFDs can map out the inputs, processes, storage, and outputs of data in a system. They are useful tools for visualizing system interactions, identifying potential bottlenecks or vulnerabilities, and for planning and improving system design.
- Datagram A self-contained, independent entity of data carrying sufficient information to be routed from the source to the destination computer without reliance on earlier exchanges between these source and destination machines and the transporting network. This means that each piece of data can take a different path to reach its intended destination, which can be useful for handling network congestion and ensuring data transmission even if parts of the network are compromised.
- Data Hiding A software development technique specifically applied to object-oriented programming, where the internal object details (data members) are hidden from external users. Data hiding helps maintain object integrity by preventing users from setting object data into an invalid or inconsistent state, enhancing system security, and reducing the likelihood of unauthorized data manipulation.
- Data in Motion Data that is actively moving through networks, either across the Internet or through private networks. It's during this active transfer process that data is often considered most vulnerable to unauthorized interception or alteration, which necessitates the use of secure transport protocols and encryption measures to ensure its safe delivery.
- Data Integrity A key principle in data security that ensures data is accurate, consistent, and reliable over its entire lifecycle. It involves maintaining the consistency, accuracy, and trustworthiness of data from the moment it is created until the point it is deleted. Measures to ensure data integrity include error checking and validation, backup, security access controls, and the implementation of specific rules and protocols.
- Data in Use Refers to data that is currently being processed or manipulated by a computer application or user. Unlike data at rest or data in transit, it's in an active state, making it potentially more vulnerable to unauthorized access or attacks, like memory scraping. Security measures for data in use include encryption and access controls. Encrypting data in active use, also known as runtime encryption, involves protecting data being processed in a computer's memory. Techniques include Trusted Execution Environments (TEEs) that create secure areas in a processor, Homomorphic Encryption that allows computations on encrypted data, and Secure Enclaves like Intel SGX, which safeguard data even if the system is compromised.
- Data Labeling The process of categorizing or tagging data, like files or digital assets, with labels that add informative context or meaning. The labels can represent different levels of sensitivity, confidentiality, or business value, and they help to enforce appropriate handling and protection measures according to the specified labels.
- Data Lake A centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having to first structure the data and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions. Data lakes are typically implemented using a flat architecture where data is tagged with metadata and unique identifiers and can be efficiently queried and analyzed.
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