Table of Contents
22 relations: Anomaly detection, Apple Inc., Big data, Cross-correlation, Differential privacy, Facial recognition system, Federated learning, Geopositioning, Google, Google Chrome, Image (mathematics), IPhone, Johannes Gehrke, Kobbi Nissim, M-ary tree, Machine learning, Ramakrishnan Srikant, Random measure, Randomized algorithm, Randomized response, Real number, Smartphone.
- Differential privacy
Anomaly detection
In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior.
See Local differential privacy and Anomaly detection
Apple Inc.
Apple Inc. is an American multinational corporation and technology company headquartered in Cupertino, California, in Silicon Valley.
See Local differential privacy and Apple Inc.
Big data
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software.
See Local differential privacy and Big data
Cross-correlation
In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other.
See Local differential privacy and Cross-correlation
Differential privacy
Differential privacy (DP) is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects. Local differential privacy and Differential privacy are information privacy and theory of cryptography.
See Local differential privacy and Differential privacy
Facial recognition system
A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.
See Local differential privacy and Facial recognition system
Federated learning
Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized.
See Local differential privacy and Federated learning
Geopositioning
Geopositioning is the process of determining or estimating the geographic position of an object.
See Local differential privacy and Geopositioning
Google LLC is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI).
See Local differential privacy and Google
Google Chrome
Google Chrome is a web browser developed by Google.
See Local differential privacy and Google Chrome
Image (mathematics)
In mathematics, for a function f: X \to Y, the image of an input value x is the single output value produced by f when passed x. The preimage of an output value y is the set of input values that produce y. More generally, evaluating f at each element of a given subset A of its domain X produces a set, called the "image of A under (or through) f".
See Local differential privacy and Image (mathematics)
IPhone
The iPhone is a smartphone produced by Apple that uses Apple's own iOS mobile operating system.
See Local differential privacy and IPhone
Johannes Gehrke
Johannes Gehrke is a Technical Fellow at Microsoft focusing on AI.
See Local differential privacy and Johannes Gehrke
Kobbi Nissim
Kobbi Nissim (קובי נסים) is a computer scientist at Georgetown University, where he is the McDevitt Chair of Computer Science.
See Local differential privacy and Kobbi Nissim
M-ary tree
In graph theory, an m-ary tree (for nonnegative integers m) (also known as n-ary, k-ary or k-way tree) is an arborescence (or, for some authors, an ordered tree) in which each node has no more than m children.
See Local differential privacy and M-ary tree
Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.
See Local differential privacy and Machine learning
Ramakrishnan Srikant
Ramakrishnan Srikant is a Google Fellow at Google.
See Local differential privacy and Ramakrishnan Srikant
Random measure
In probability theory, a random measure is a measure-valued random element.
See Local differential privacy and Random measure
Randomized algorithm
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure.
See Local differential privacy and Randomized algorithm
Randomized response
Randomised response is a research method used in structured survey interview.
See Local differential privacy and Randomized response
Real number
In mathematics, a real number is a number that can be used to measure a continuous one-dimensional quantity such as a distance, duration or temperature.
See Local differential privacy and Real number
Smartphone
A smartphone, often simply called a phone, is a mobile device that combines the functionality of a traditional mobile phone with advanced computing capabilities.
See Local differential privacy and Smartphone
See also
Differential privacy
- Additive noise differential privacy mechanisms
- Differential privacy
- Differentially private analysis of graphs
- Exponential mechanism
- List of implementations of differentially private analyses
- Local differential privacy
- Reconstruction attack
References
Also known as Local DP.