Differentially private data release for data mining

By Noman Mohammed, Rui Chen, Benjamin C. M. Fung, Philip S. Yu. 2011

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Data Synthesis based on Generative Adversarial Networks (2018, background): “One more related privacy concept is -differential private data release [26].”
Security and privacy challenges in smart cities (2018, background): “mising the information utility for classification analysis [24].”
Differentially private sequential data publication via variable-length n-grams (2012, methodology): “[17] propose a generalization-based sanitization algorithm for relational data with the goal of classification analysis.”
A Semi-Supervised Learning Approach to Differential Privacy (2013, background): “Let D = {D1, . . . ,Dk} denote the set of domains, each of which could be categorical or numeric.”
Near-optimal Differentially Private Principal Components (2012, background): “Suppose A is a positive semidefinite matrix whose first k eigenvalues are distinct.”
Differentially Private Matrix Factorization (2015, background): “We formally give the definition of different privacy below [Mohammed et al., 2011].”
Differential Privacy in Telco Big Data Platform (2015, methodology): “…on DP from industrial perspectives: • Implementation in telco big data platform: We broadly categorize the recent DP implementations for DTs into three basic architectures: 1) Data Publication Architecture [25, 39]; 2) Separated Architecture [7]; and 3) Hybridized Architecture [7, 13].”
Secure Distributed Framework for Achieving ε-Differential Privacy (2012, background): “Blue-collar Any_Sex [60-99) ∪Cuti = {Any_Job, Any_Sex, [1-99)} Any_Job {White-collar, Blue-collar} ∪Cuti = {White-collar,Blue-collar, Any_Sex, [1,99)}”
Differentially Private Data Publishing and Analysis: A Survey (2017, neutral): “[44] proposed the anonymized algorithm DiffGen to preserve privacy for data mining purposes.”
Differential Privacy and Machine Learning: a Survey and Review (2014, methodology): “[41] first partitions the sample space using the exponential mechanism and then adds noise to the counts using the Laplacian mechanism.”
PrivBasis: Frequent Itemset Mining with Differential Privacy (2012, background): “More recently, differential privacy has been adapted to release accurate data mining models and results [19, 29, 8].”
Convergence Rates for Differentially Private Statistical Estimation (2012, background): “…(Dwork et al., 2006b) is a strong, cryptographically-motivated deﬁnition of privacy which has gained signiﬁcant attention in the machine-learning and data-mining communities over the past few years (McSherry & Mironov, 2009; Chaudhuri et al., 2011; Friedman & Schuster, 2010; Mohammed et al., 2011).”
Correlated network data publication via differential privacy (2013, methodology): “Previous works [9, 30] normally require a data publisher to specify the height.”
Differential privacy based on importance weighting (2013, methodology): “…dataset into several groups according to similarity or entropy (this step either involves randomness in order not to destroy privacy, or is data-independent), add noise to the counts of samples in each group, and publish the noisy counts (Xiao et al. 2010; Mohammed et al. 2011; Ding et al. 2011).”
Differentially Private K-Means Clustering (2015, background): “Mohammed et al. [29] tailored the non-interactive data release for construction of decision trees.”