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differential privacy via a Truncated and Normalized Laplace Mechanism

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Journal of Computer Science & Technology 2022年第2期37卷 369-388页

作者： William Croft Jorg-Rudiger Sack Wei Shi School of Computer Science Carleton UniversityOttawa K1S 5B6Canada School of Information Technology Carleton UniversityOttawa K1S 5B6Canada

When querying databases containing sensitive information,the privacy of individuals stored in the database has to be *** guarantees are provided by differentially private mechanisms which add controlled noise to the query ***,most such mechanisms do not take into consideration the valid range of the query being ***,noisy responses that fall outside of this range may potentially be *** rectify this and therefore improve the utility of the mechanism,the commonly-used Laplace distribution can be truncated to the valid range of the query and then ***,such a data-dependent operation of normalization leaks additional information about the true query response,thereby violating the differential privacy ***,we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace *** adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint *** provide derivations of the optimal scaling parameter(i.e.,the minimal value that preserves differential privacy)for each class or provide an approximation *** a result of this work,one can use the Laplace distribution to answer queries in a range-adherent and differentially private *** demonstrate the benefits of our proposed method of normalization,we present an experimental comparison against other range-adherent *** show that our proposed approach is able to provide improved utility over the alternative mechanisms.

关键词： differential privacy Laplace mechanism query range constraint

differential privacy Preserving Dynamic Data Release Scheme Based on Jensen-Shannon Divergence

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China Communications 2022年第6期19卷 11-21页

作者： Ying Cai Yu Zhang Jingjing Qu Wenjin Li Department of Computer Science and Technology Beijing Key Laboratory of Internet Culture and Digital Dissemination ResearchBeijing Information Science and Technology UniversityBeijing 100101China

Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released,but they may contain user's sensitive ***,how to preserve the user's privacy before their release is critically important yet *** privacy(DP)is well-known to provide effective privacy protection,and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP ***,this scheme may result in high cumulative errors and lower the data *** address this problem,in this paper,we apply Jensen-Shannon(JS)divergence to design the OPTICS(Ordering Points To Identify The Clustering Structure)*** uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release *** comparing the difference with a threshold,only when the difference is greater than the threshold,can we apply OPTICS to publish DP protected data *** experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.

关键词： differential privacy dynamic data release Jensen-Shannon divergence

Embedding differential privacy in decision tree algorithm with different depths

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Science China(Information Sciences) 2017年第8期60卷 136-150页

作者： Xuanyu BAI Jianguo YAO Mingxuan YUAN Ke DENG Xike XIE Haibing GUAN Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University

differential privacy(DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining(DM)logic is considered in the DP implementation. However, although one-step DM computation for decision tree(DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo(MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation(previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large(e.g., ? = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity.

关键词： differential privacy decision tree exponential mechanism exhaustive search MCMC

Community-Preserving Social Graph Release with Node differential privacy

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Journal of Computer Science & Technology 2023年第6期38卷 1369-1386页

作者：张森倪巍伟付楠 School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 211189China

The goal of privacy-preserving social graph release is to protect individual privacy while preserving data *** structure,which is an important global pattern of nodes,is a crucial data utility as it is fundamental to many graph analysis ***,most existing methods with differential privacy(DP)commonly fall into edge-DP to sacri-fice security in exchange for ***,they reconstruct graphs from the local feature-extraction of nodes,resulting in poor community *** by this,we develop PrivCom,a strict node-DP graph release algorithm to maximize the utility on the community structure while maintaining a higher level of *** this algorithm,to reduce the huge sensitivity,we devise a Katz index based private graph feature extraction method,which can capture global graph structure features while greatly reducing the global sensitivity via a sensitivity regulation ***,under the condition that the sensitivity is fixed,the feature captured by the Katz index,which is presented in matrix form,requires privacy budget *** a result,plenty of noise is injected,mitigating global structural *** bridge this gap,we de-sign a private eigenvector estimation method,which yields noisy eigenvectors from extracted low-dimensional ***,a dynamic privacy budget allocation method with provable utility guarantees is developed to preserve the inherent relationship between eigenvalues and eigenvectors,so that the utility of the generated noise Katz matrix is well ***,we reconstruct the synthetic graph via calculating its Laplacian with the noisy Katz *** results confirm our theoretical findings and the efficacy of PrivCom.

关键词： differential privacy social graph community structure private eigenvector

Efficient Publication of Distributed and Overlapping Graph Data Under differential privacy

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Tsinghua Science and Technology 2022年第2期27卷 235-243页

作者： Xu Zheng Lizong Zhang Kaiyang Li Xi Zeng the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China China Electronics Technology Cyber Security Co.Ltd. Chengdu 610000China

Graph data publication has been considered as an important step for data analysis and *** data,which provide knowledge on interactions among entities,can be locally generated and held by distributed data *** data are usually sensitive and private,because they may be related to owners’personal activities and can be hijacked by adversaries to conduct inference *** solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed ***,this work proposes a novel framework for distributed graph *** this framework,differential privacy is applied to justify the safety of the published *** includes four phases,i.e.,graph combination,plan construction sharing,data perturbation,and graph *** published graph selection is guided by one data coordinator,and each graph is perturbed carefully with the Laplace *** problem of graph selection is formulated and proven to be ***,a heuristic algorithm is proposed for *** correctness of the combined graph and the differential privacy on all edges are *** study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

关键词： graph data distributed data publication differential privacy

A differential privacy Protection Protocol Based on Location Entropy

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Tsinghua Science and Technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

Sarve:synthetic data and local differential privacy for private frequency estimation

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Cybersecurity 2022年第4期5卷 97-116页

作者： Gatha Varmal Ritu Chauhan Dhananjay Singh Amity Institute of Information Technology Amity UniversityNoidaIndia Center for Computational Biology and Bioinformatics Amity UniversityNoidaIndia Department of Electronics Engineering HUFSSeoulKorea

The collection of user attributes by service providers is a double-edged *** are instrumental in driving statistical analysis to train more accurate predictive models like *** analysis of the collected user data includes frequency estimation for categorical ***,the users deserve privacy guarantees against inadvertent identity *** algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their *** propose Sarve,a frequency oracle that used Randomized Aggregatable privacy-Preserving Ordinal Response(RAPPOR)and Hadamard Response(HR)for randomization in combination with fake *** design of a service-oriented architecture must consider two types of complexities,namely computational and *** functions of such systems aim to minimize the two complexities and therefore,the choice of privacy-enhancing methods must be a calculated *** variant of RAPPOR we had used was realized through bloom flters.A bloom filter is a memory-efficient data structure that offers time complexity of O(1).On the other hand,HR has been proven to give the best communication costs of the order of log(b)for b-bits ***,Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original *** also implemented an adaptive solution enhanced from the work of Arcolezi et *** use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacyregimes.

关键词： Synthetic data differential privacy Frequency estimation Frequency oracle privacy

Achieving differential privacy of Genomic Data Releasing via Belief Propagation

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Tsinghua Science and Technology 2018年第4期23卷 389-395页

作者： Zaobo He Yingshu Li Ji Li Kaiyang Li Qing Cai Yi Liang Department of Computer Science Georgia State University Atlanta GA 30303 USA School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China

privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs （Single Nucleotide Polymorphisms）, which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.

关键词： differential privacy SNP/trait associations belief propagation factor graph data releasing

Publishing Social Graphs with differential privacy Guarantees Based on wPINQ

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Chinese Journal of Electronics 2019年第2期28卷 273-279页

作者： LI Xiaoye YANG Jing SUN Zhenlong ZHANG Jianpei College of Computer Science and Technology Harbin Engineering University College of Computer and Control Enginewaeering Qiqihar University

To publish social graphs with differential privacy guarantees for reproducing valuable results of scientific researches, we study a workflow for graph synthesis and propose an improved approach based on weighted privacy integrated query(wPINQ). The workflow starts with a seed graph to fit the noisy degree sequence, which essentially is the 1K-graph. In view of the inaccurate assortativity coefficient, we truncate the workflow to replace the seed graph with an optimal one by doing target 1K-rewiring while preserving the 1K-distribution. Subsequently, Markov chain Monte Carlo employs the new seed graph as the initial state, and proceeds step by step guided by the information of Triangles by intersect to increase the number of triangles in the synthetic graphs. The experimental results show that the proposed algorithm achieves better performance for the published social graphs.

关键词： differential privacy Social networks weighted privacy integrated query(wPINQ) dK-graph model