Privacy preserving mining of association rules

By Alexandre V. Evfimievski, Rayadurgam Srikant, Rakesh Agrawal, Johannes Gehrke. 2002

1,092 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Privacy & Security

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Citations in the corpus

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation (2021, background): “More recently, there has been a great interest in the local model of DP (Kasiviswanathan et al., 2011; Evfimievski et al., 2004; Warner, 1965) where the data is perturbed on the client side before it is collected by a service provider.”
Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Vaidya and Clifton (2002); Evﬁmievski et al. (2004); Dwork and Nissim (2004) are among the early studies that investigate the privacy issue of querying vertically partitioned data.”
Federated Heavy Hitters Discovery with Differential Privacy (2019, methodology): “The local model of DP (Warner, 1965; Evfimievski et al., 2004; Kasiviswanathan et al., 2011) addresses the above concerns by requiring users to perturb their data locally before sharing it with a service provider.”
Marginal Release Under Local Differential Privacy (2017, positive): “tead captures the case when each user independently (but collaboratively) releases information on their input through an instance of a DP algorithm. This model was ﬁrst suggested by Evﬁmievski et al. [15] under the name of -ampliﬁcation, with an application to mining association rules. Duchi et al. ”
Composition attacks and auxiliary information in data privacy (2008, methodology): “Randomization-based schemes introduce uncertainty either by randomly perturbing the raw data (a technique called input perturbation , randomized response , e.g., [34, 2, 16]), or post-randomization , e.g., [32]), or by injecting randomness into the algorithm used to analyze the data (e.g., [6, 28]).”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, methodology): “In general, there are two different approaches: one is to use data distortion approach, in which data are disguised by the added noises [1, 2, 13, 14, 22, 23].”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “It has been shown in [78] that such an approach is more robust since the noise correlates with the stream behavior, and it is more difﬁcult to create effective adversarial attacks with the use of correlation analysis techniques.”
Assessing global disclosure risk in masked microdata (2004, background): “Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”
Using randomized response techniques for privacy-preserving data mining (2003, methodology): “proposed an approach to conduct privacy preserving association rule mining based on randomized response techniques [5].”
Anonymity-preserving data collection (2005, methodology): “Di(cid:11)erent random perturbation methods have been proposed and applied in di(cid:11)erent data mining algorithms [2, 1, 12, 28, 11, 9].”
Privacy-preserving SVM classification (2008, neutral): “Received April 12, 2006 Revised November 14, 2006 Accepted January 26 , 2007 2 J. Vaidya et al”
k -Anonymous Data Mining: A Survey (2008, background): “been married M [40-100) 17 been married F [1-40) 2 been married F [40-100) 9 never married M [40-100) 26”
Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning (2017, background): “, [8], [9]) have focused on additive or multiplicative perturbations of individual samples, which might affect certain relationships among different samples in the database.”
Sharemind: A Framework for Fast Privacy-Preserving Computations (2008, background): “So far the focus has been on randomised response techniques [2,1,13].”