Cryptographic techniques for privacy-preserving data mining

By Benny Pinkas. 2002

496 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Privacy Preserving Data Mining (2015, background): “Various ways to handle PPDM problems including the cryptographic approaches available in [4][3].”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, background): “Another general approach is the secure multi-party computation approach [7,12,18, 21 ,25].”
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, background): “The homomorphic cryptosystems also enable a variety of privacy-preserving machine learning and data mining [7, 14, 25, 39, 43, 44, 54, 57, 61] because they can make some weights perfectly secret by encrypting them.”
Differential privacy with compression (2009, background): “Recent approaches to privacy include data swapping [14], k-anonymity [22], and cryptographic approaches (for instance, [19, 13]).”
Privacy-Preserving Data Mining: Models and Algorithms (2008, background): “A survey of such methods may be found in [14].”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “We refer readers interested in PPDDM to work by [Clifton et al. 2002; Kantarcioglu 2008; Pinkas 2002; Vaidya 2008; Wright et al. 2005].”
Tools for privacy preserving distributed data mining (2002, background): “A detailed discussion of Secure Multiparty Computation is given elsewhere in this issue[ 14 ], and we encourage readers who want a deep understanding of the following material to start with that article.”
Deriving private information from randomized data (2005, background): “Several SMC-based privacy-preserving data mining schemes have been proposed [17, 19, 23, 24, 5].”
Secure function evaluation based on secret sharing and homomorphic encryption (2009, background): “Let ξ(·) be an additively homomorphic mapping, i.e., ξ(m1 + m2) = ξ(m1)ξ(m2) and ξ(km1) = ξ(m1)k for integer messages m1, m2 and a constant integer k. Examples of such a mapping include the Paillier cryptosystem [9] and the Benaloh cryptosystem[10].”
Improved Garbled Circuit: Free XOR Gates and Applications (2008, background): “It is known that the problem of PF-SFE can be reduced to the “regular” SFE [24,23].”
DJoin: Differentially Private Join Queries over Distributed Databases (2012, unknown): “There are specialized protocols for other private multi-party operations, e.g., for decisiontree learning [29], and some of these have been adapted for differential privacy, e.g., [39].”
Oblivious Neural Network Computing via Homomorphic Encryption (2007, background): “by means of homomorphic encryption, a limited amount of interaction between the NN owner and the user is introduced; however, in contrast to previous works in the general area of privacy preserving data mining [3], the interaction is kept to a minimum and no resort to sophisticated multiparty comput”
On Privacy-Preservation of Text and Sparse Binary Data with Sketches (2007, methodology): “A survey on cryptographic methods has been discussed in [19].”
A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements (2011, background): “Privacy preserving data mining [63, 64] 9 9 9”
Distributed Differential Privacy and Applications (2015, background): “, for decision-tree learning (Pinkas, 2002), and some of these have been adapted for differential privacy, e.”
Reversible Image Watermarking Using Interpolation Technique (2010, methodology): “DE, also known as a kind of integer wavelet transform, was ﬁrst proposed by Tian [3].”