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Differential Privacy for Databases
Foundations and Trends in Databases, 2021Differential privacy is a promising approach to formalizing privacy—that is, for writing down what privacy means as a mathematical equation. This book is provides overview of differential privacy techniques for answering database-style queries. Within this area, we describe useful algorithms and their applications, and systems and tools that implement ...
Joseph P. Near, Xi He 0001
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2006
In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved.
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In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved.
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Differential Privacy in Practice
2012Differential privacy (DP) has attracted considerable attention as the method of choice for releasing aggregate query results making it hard to infer information about individual records in the database. The most common way to achieve DP is to add noise following Laplace distribution.
Maryam Shoaran +2 more
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On syntactic anonymity and differential privacy
2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), 2013Recently, there has been a growing debate over approaches for handling and analyzing private data. Research has identified issues with syntactic anonymity models. Differential privacy has been promoted as the answer to privacy-preserving data mining. We discuss here issues involved and criticisms of both approaches, and conclude that both have their ...
Chris Clifton, Tamir Tassa
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Asymmetric Differential Privacy
2022 IEEE International Conference on Big Data (Big Data), 2022Shun Takagi +3 more
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Linking Differential Identifiability with Differential Privacy
2018The problem of preserving privacy while mining data has been studied extensively in recent years because of its importance for enabling sharing data sets. Differential Identifiability, parameterized by the probability of individual identification \(\rho \), was proposed to provide a solution to this problem.
Anis Bkakria +2 more
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A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
ACM Computing Surveys, 2023Alberto Blanco-Justicia +2 more
exaly
Applications of Differential Privacy in Social Network Analysis: A Survey
IEEE Transactions on Knowledge and Data Engineering, 2021Honglu Jiang, Jian Pei, Dongxiao Yu
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A Survey on Differential Privacy for Unstructured Data Content
ACM Computing Surveys, 2022Jinjun Chen
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