Discovering frequent patterns in sensitive data

By Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta. 2010

284 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Privacy & Security

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Citations in the corpus

Federated Heavy Hitters Discovery with Differential Privacy (2019, background): “[6] Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta.”
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “For example, [24, 26, 16, 31] address the problem of publishing a subset of the data contained in a search log with differential privacy guarantees; [27] and [6] propose approaches for frequent item identification; [14] propose an approach for monitoring aggregated web browsing activities; and so on”
Tight Lower Bounds for Differentially Private Selection (2017, background): “Selection problems appear in many natural statistical problems, including private multiple hypothesis testing [1], sparse linear regression [2], [3], finding frequent itemsets [4], and as subroutines in algorithms for answering exponentially many statistical queries [5], [6], [7], [8], [9], approxim”
Scalable Private Learning with PATE (2018, background): “The most commonly used differentially private selection mechanisms are exponential mechanism (McSherry & Talwar, 2007) and LNMax (Bhaskar et al., 2010).”
Functional Mechanism: Regression Analysis under Differential Privacy (2012, methodology): “For instance, the exponential mechanism has been applied for the publication of audition results [21], coresets [10], frequent patterns [3], decision trees [11], support vector machines [25], and synthetic datasets [7,16].”
Privacy via the Johnson-Lindenstrauss Transform (2012, methodology): “We adopt a rigorous approach to privacy introduced by Dwork et al. [12], which has gained widespread recognition in recent years (see a survey [10]), and has been used to demonstrate the feasibility of privacy-preserving data releases [3, 20, 23, 24].”
Private selection from private candidates (2018, background): “Some examples include private PAC learning [20], private frequent itemset mining [4], private PCA [7, 19] and private multiple hypothesis testing [14, 35].”
Mining Frequent Patterns with Differential Privacy (2013, methodology): “In this setting, only few works have been proposed to mine frequent patterns [3, 16, 25].”
Evaluating Differentially Private Machine Learning in Practice (2019, background): “Some of the early data analytics works on frequent pattern mining [7,41], decision trees [21], private record linkage [30] and recommender systems [48] were able to achieve both high utility and privacy with settings close to 1.”
Composable and versatile privacy via truncated CDP (2018, unknown): “This algorithm readily generalizes to the gap version of the “topm problem” (more commonly called top-k) which has been studied extensively [2, 4, 7, 23]: We simply set f (x , j) = max{l(x , j) − l(x ,σx (m + 1)), 0} instead.”
Privacy-Preserving Collaborative Deep Learning with Irregular Participants. (2018, methodology): “We can further signiﬁcantly reduce the running time by implementing the sampling step on a static balanced binary tree as suggested in [41].”
A Stability-based Validation Procedure for Differentially Private Machine Learning (2013, background): “Theorem 2 (End-to-end Privacy Guarantees) If the conditions in Theorem 1 hold, and if T is α1-differentially private, then Algorithm 2 is (α1 + α2, δ)-differentially private.”
Private False Discovery Rate Control (2015, background): “To determine how to add noise to ensure privacy, we study the sensitivity of a p -value, a measure of how much the p -value can change between adjacent datasets (Section 4.1) 5 .”
Differentially Private Data Publishing and Analysis: A Survey (2017, methodology): “[61], [63], [64] and [62] tried to merge some candidates into groups using different methods.”