Limiting privacy breaches in privacy preserving data mining

By Alexandre V. Evfimievski, Johannes Gehrke, Rayadurgam Srikant. 2003

972 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Privacy & Security

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In Information Systems

In Privacy & Security

Citations in the corpus

Practical and Private (Deep) Learning without Sampling or Shuffling (2021, background): “This improvement is significant because it distinguishes centrally private OCO from locally private (Warner, 1965; Evfimievski et al., 2003; Kasiviswanathan et al., 2008) OCO4.”
Protection Against Reconstruction and Its Applications in Private Federated Learning (2018, background): “An alternative to these approaches is to use locally private algorithms [67, 36, 31], in which an individual keeps his or her data private even from the data collector.”
Distributed Differential Privacy via Shuffling (2018, background): “In the local model [33, 18, 14], each individual applies a differentially private algorithm locally to their data and shares only the output of the algorithm—called a report or response—with a server that aggregates users’ reports.”
Collecting Telemetry Data Privately (2017, background): “In the context of telemetry collection one typically considers algorithms that exhibit differential privacy in the local model [12, 14, 7, 5, 3, 18], also called randomized response model [19], γ-amplification [13], or FRAPP [1].”
Local, Private, Efficient Protocols for Succinct Histograms (2015, background): “We focus on protocols that provide differential privacy [6] (or, equivalently in the local model, -amplification [10] or FRAPP [1]).”
Privacy Aware Learning (2012, methodology): “This is similar to the worst-case information approach of Evﬁmievski et al. [13], which limits privacy breaches.”
Distributed Private Data Analysis: On Simultaneously Solving How and What (2008, background): “A criterion for functions that preserve privacy of individual entries, differential privacy, has evolved in a sequence of recent works [7, 15, 14, 2, 11, 8, 9].”
Minimax Optimal Procedures for Locally Private Estimation (2016, methodology): “…that are problematic for other formalisms, have been used to make the case for differential privacy within the computer science literature; see, for example, the articles Evfimievski, Gehrke, and Srikant (2003); Dwork et al. (2006); Barak et al. (2007); Ganta, Kasiviswanathan, and Smith (2008).”
A learning theory approach to non-interactive database privacy (2008, neutral): “Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity General Terms Algorithms, Security, Theory Keywords non-interactive database privacy, learning theory”
Separating Local & Shuffled Differential Privacy via Histograms (2019, background): “The local model of private computation has minimal trust assumptions: each user executes a randomized algorithm on their data and sends the output message to an analyzer [17, 19, 11].”
Composition attacks and auxiliary information in data privacy (2008, background): “Recent work has greatly expanded the applicability of differential privacy and its re-laxations, both in the theoretical [15, 6, 14, 4, 28] and applied [17, 3, 25] literature.”
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information (2018, background): “In response to these challenges, researchers in the statistics, databases, and computer science communities have studied diﬀerential privacy [54, 25, 24, 23, 27, 21] as a formalization of disclosure risk limitation, providing strong privacy guarantees.”
A ug 2 01 4 Local Privacy , Data Processing Inequalities , and Minimax Rates (2018, methodology): “We also consider the non-interactive case [50, 27] where Zi depends only on Xi (see the right side of Figure 1); here the bound (1) reduces to”
Discovering frequent patterns in sensitive data (2010, background): “The work of [6] is an improvement over [12].”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, background): “This work was extended in [12], where a framework for quantifying privacy breaches was introduced.”