Privacy Preserving Data Mining

By Yehuda Lindell, Benny Pinkas. 2000

177 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Hybrid Approach to Privacy-Preserving Federated Learning (2018, methodology): “Aggregator The honest-but-curious or semi-honest adversarial model is commonly used in the ﬁeld of SMC since its introduction in (Goldreich 2009) and application to data mining in (Lindell and Pinkas 2000).”
Blockchain-Based Privacy Preserving Deep Learning (2018, methodology): “Techniques based on Secure Multi-party Computation (SMC) can help protect intermediate steps of the computation, such as decision trees [13], linear regression functions [14], association rules [15], Naive Bayes classi-ﬁers [16], and k-means clustering [17],when multiple parties perform…”
Privacy integrated queries (2010, methodology): “The cryptography community uses the phrase “privacy preserving data mining”, see [ 10 ], but for cryptographically secure function evaluation, which reveals the result of the computation but no further information about the inputs other than what the result may imply.”
Privacy preserving mining of association rules (2002, background): “Another approach, presentedin [ 26 ], does not use randomization, but makes use of cryptographic oblivious functions during tree construction to preserve privacy of two data sources.”
Building decision tree classifier on private data (2002, background): “In early work on such privacy preserving data mining problem Lindell and Pinkas (Lindell & Pinkas 2000) propose a solution to the privacy preserving classification problem using the oblivious transfer protocol, a powerful tool developed by the secure multi-party computation studies.”
Privacy-preserving cooperative scientific computations (2001, background): “Those few applied problems include Private Information Retrieval problem (PIR) [12, 3, 11, 10, 13, 17, 14, 9], Joint digital signature [21, 5] and joint decryption, elections over the Internet, electronic bidding [2], and privacy-preserving data mining [16, 1].”
Extending Oblivious Transfers Efficiently (2003, background): “This is also the case for more specialized or practically-oriented protocols (e.g., [30,29,17, 26 ,12,27]), where oblivious transfers typically form the eciency bottleneck.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “The work in [80] discusses the construction of a popular decision tree induction method called ID3 with the use of approximations of the best splitting attributes.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, unknown): “The SMC ideas have also been applied for privacy preserving distributed decision tree induction [ 36 ], [37], naive Bayes classification [38] for horizontally partitioned data, privacy preserving Bayesian network structure computation for vertically partitioned data [39], and many else.”
Privacy-preserving distributed mining of association rules on horizontally partitioned data (2004, methodology): “The other approach uses cryptographic tools to build decision trees [ 8 ].”
Distributed Data Mining with Differential Privacy (2011, background): “Remark Even though secure equality and secure modulo reduction are not as efficient as secure sum, they are still much better than existing protocols that rely heavily on cryptographic primitives to securely induce the decision tree [2].”
Collaborative, Privacy-Preserving Data Aggregation at Scale (2010, background): “More efficient solutions exist for special cases of the PDA problem, such as secure set intersection [13, 30, 27, 16, 26, 15, 23, 10].”
Airavat: Security and Privacy for MapReduce (2010, background): “Cryptographically secure multi-party computation(as in [ 31 ]) ensures that a distributed protocol leaks no more information about the individual inputs than is revealed by the result of the computation.”
Fairness in Machine Learning: A Survey (2020, background): “In particular, sensitive data disclosure is a long-standing challenge in protecting citizen anonymity when data is published and/or analysed [8, 105, 184].”