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Implementations Of Differentially Private Analyses
Since the advent of differential privacy, a number of systems supporting differentially private data analyses have been implemented and deployed. This article tracks real-world deployments, production software packages, and research prototypes. Real-world deployments Production software packages These software packages purport to be usable in production systems. They are split in two categories: those focused on answering statistical queries with differential privacy, and those focused on training machine learning models with differential privacy. Statistical analyses Machine learning Research projects and prototypes Attacks on implementations In addition to standard defects of software artifacts that can be identified using testing or fuzzing, implementations of differentially private mechanisms may suffer from the following vulnerabilities: * Subtle algorithmic or analytical mistakes. * Timing side-channel attacks. In contrast with timing attacks again ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Privacy
Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible ev ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PyTorch
PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, Uber's Pyro, Hugging Face's Transformers, PyTorch Lightning, and Catalyst. PyTorch provides two high-level features: * Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) * Deep neural networks built on a tape-based automatic differentiation system History Meta (formerly known as Facebook) operates both ''PyTorch'' and ''Convolutional Architecture for Fast Feature Embedding'' (Caffe2), but models defined by the two framewor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Secure Multi-party Computation
Secure multi-party computation (also known as secure computation, multi-party computation (MPC) or privacy-preserving computation) is a subfield of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. Unlike traditional cryptographic tasks, where cryptography assures security and integrity of communication or storage and the adversary is outside the system of participants (an eavesdropper on the sender and receiver), the cryptography in this model protects participants' privacy from each other. The foundation for secure multi-party computation started in the late 1970s with the work on mental poker, cryptographic work that simulates game playing/computational tasks over distances without requiring a trusted third party. Note that traditionally, cryptography was about concealing content, while this new type of computation and protocol is about concealing partial information about data while comp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Privacy
Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible ev ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Denormal Numbers
In computer science, subnormal numbers are the subset of denormalized numbers (sometimes called denormals) that fill the underflow gap around zero in floating-point arithmetic. Any non-zero number with magnitude smaller than the smallest normal number is ''subnormal''. :: ''Usage note: in some older documents (especially standards documents such as the initial releases of IEEE 754 and the C language), "denormal" is used to refer exclusively to subnormal numbers. This usage persists in various standards documents, especially when discussing hardware that is incapable of representing any other denormalized numbers, but the discussion here uses the term subnormal in line with the 2008 revision of IEEE 754.'' In a normal floating-point value, there are no leading zeros in the significand ( mantissa); rather, leading zeros are removed by adjusting the exponent (for example, the number 0.0123 would be written as ). Conversely, a denormalized floating point value has a significand with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Double-precision Floating-point Format
Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. Floating point is used to represent fractional values, or when a wider range is needed than is provided by fixed point (of the same bit width), even if at the cost of precision. Double precision may be chosen when the range or precision of single precision would be insufficient. In the IEEE 754-2008 standard, the 64-bit base-2 format is officially referred to as binary64; it was called double in IEEE 754-1985. IEEE 754 specifies additional floating-point formats, including 32-bit base-2 ''single precision'' and, more recently, base-10 representations. One of the first programming languages to provide single- and double-precision floating-point data types was Fortran. Before the widespread adoption of IEEE 754-1985, the representation and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Additive Noise Mechanisms
Adding controlled noise from predetermined distributions is a way of designing differentially private mechanisms. This technique is useful for designing private mechanisms for real-valued functions on sensitive data. Some commonly used distributions for adding noise include Laplace and Gaussian distributions. Definitions Let \mathcal be a collection of all datasets and f:\mathcal \to \R be a real-valued function. The ''sensitivity'' of a function, denoted \Delta f, is defined by : \Delta f=\max , f(x)-f(y) , , where the maximum is over all pairs of datasets x and y in \mathcal differing in at most one element. For functions with higher dimensions, the sensitivity is usually measured under \ell_1 or \ell_2 norms. Throughout this article, \mathcal is used to denote a randomized algorithm that releases a sensitive function f under the \epsilon- (or (\epsilon,\delta)-) differential privacy. Mechanisms for Real-Valued Functions Laplace Mechanism Introduced by Dwork et al., t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Leaky Abstraction
In software development, a leaky abstraction is an abstraction that leaks details that it is supposed to abstract away. As coined by Joel Spolsky, the Law of Leaky Abstractions states: This statement highlights a particularly problematic cause of software defects: the reliance of the software developer on an abstraction's infallibility. Spolsky's article gives examples of an abstraction that works most of the time, but where a detail of the underlying complexity cannot be ignored, thus leaking complexity out of the abstraction back into the software that uses the abstraction. History The term "leaky abstraction" was popularized in 2002 by Joel Spolsky. An earlier paper by Kiczales describes some of the issues with imperfect abstractions and presents a potential solution to the problem by allowing for the customization of the abstraction itself. Effect on software development As systems become more complex, software developers must rely upon more abstractions. Each abstraction ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Timing Attack
In cryptography, a timing attack is a side-channel attack in which the attacker attempts to compromise a cryptosystem by analyzing the time taken to execute cryptographic algorithms. Every logical operation in a computer takes time to execute, and the time can differ based on the input; with precise measurements of the time for each operation, an attacker can work backwards to the input. Finding secrets through timing information may be significantly easier than using cryptanalysis of known plaintext, ciphertext pairs. Sometimes timing information is combined with cryptanalysis to increase the rate of information leakage. Information can leak from a system through measurement of the time it takes to respond to certain queries. How much this information can help an attacker depends on many variables: cryptographic system design, the CPU running the system, the algorithms used, assorted implementation details, timing attack countermeasures, the accuracy of the timing measurements, et ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fuzzing
In programming and software development, fuzzing or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. The program is then monitored for exceptions such as crashes, failing built-in code assertions, or potential memory leaks. Typically, fuzzers are used to test programs that take structured inputs. This structure is specified, e.g., in a file format or protocol and distinguishes valid from invalid input. An effective fuzzer generates semi-valid inputs that are "valid enough" in that they are not directly rejected by the parser, but do create unexpected behaviors deeper in the program and are "invalid enough" to expose corner cases that have not been properly dealt with. For the purpose of security, input that crosses a trust boundary is often the most useful. For example, it is more important to fuzz code that handles the upload of a file by any user than it is to fuzz the code tha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Software Testing
Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but not necessarily limited to: * analyzing the product requirements for completeness and correctness in various contexts like industry perspective, business perspective, feasibility and viability of implementation, usability, performance, security, infrastructure considerations, etc. * reviewing the product architecture and the overall design of the product * working with product developers on improvement in coding techniques, design patterns, tests that can be written as part of code based on various techniques like boundary conditions, etc. * executing a program or application with the intent of examining behavior * reviewing the deployment infrastructure a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
