Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

Local differential privacy(LDP)approaches to collecting sensitive information for frequent itemset mining(FIM)can reliably guarantee privacy.Most current approaches to FIM under LDP add"padding and sampling"steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of items.The current state-of-the-art approach,namely set-value itemset mining(SVSM),must balance variance and bias to achieve accurate results.Thus,an unbiased FIM approach with lower variance is highly promising.To narrow this gap,we propose an Item-Level LDP frequency oracle approach,named the Integrated-with-Hadamard-Transform-Based Frequency Oracle(IHFO).For the first time,Hadamard encoding is introduced to a set of values to encode all items into a fixed vector,and perturbation can be subsequently applied to the vector.An FIM approach,called optimized united itemset mining(O-UISM),is pro-posed to combine the padding-and-sampling-based frequency oracle(PSFO)and the IHFO into a framework for acquiring accurate frequent itemsets with their frequencies.Finally,we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

Improving Parameter Estimation and Defensive Ability of Latent Dirichlet Allocation Model Training Under Rényi Differential Privacy

Latent Dirichlet allocation(LDA)is a topic model widely used for discovering hidden semantics in massive text corpora.Collapsed Gibbs sampling(CGS),as a widely-used algorithm for learning the parameters of LDA,has the risk of privacy leakage.Specifically,word count statistics and updates of latent topics in CGS,which are essential for parameter estimation,could be employed by adversaries to conduct effective membership inference attacks(MIAs).Till now,there are two kinds of methods exploited in CGS to defend against MIAs:adding noise to word count statistics and utilizing inherent privacy.These two kinds of methods have their respective limitations.Noise sampled from the Laplacian distribution sometimes produces negative word count statistics,which render terrible parameter estimation in CGS.Utilizing inherent privacy could only provide weak guaranteed privacy when defending against MIAs.It is promising to propose an effective framework to obtain accurate parameter estimations with guaranteed differential privacy.The key issue of obtaining accurate parameter estimations when introducing differential privacy in CGS is making good use of the privacy budget such that a precise noise scale is derived.It is the first time that R′enyi differential privacy(RDP)has been introduced into CGS and we propose RDP-LDA,an effective framework for analyzing the privacy loss of any differentially private CGS.RDP-LDA could be used to derive a tighter upper bound of privacy loss than the overestimated results of existing differentially private CGS obtained byε-DP.In RDP-LDA,we propose a novel truncated-Gaussian mechanism that keeps word count statistics non-negative.And we propose distribution perturbation which could provide more rigorous guaranteed privacy than utilizing inherent privacy.Experiments validate that our proposed methods produce more accurate parameter estimation under the JS-divergence metric and obtain lower precision and recall when defending against MIAs.

Partial Label Learning via Conditional-Label-Aware Disambiguation

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling.Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels.Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.