Differentially Private Data Publishing and Analysis: A Survey

Zhu, Tianqing; Li, Gang; Zhou, Wanlei; Yu, Philip S.

doi:10.1109/tkde.2017.2697856

Cited by 264 publications

(126 citation statements)

References 89 publications

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“…Besides adding noise prior to computing PCA, we also add noise to the output of PCA. According to differential privacy parallel composition [20], the whole dataset is private as long as each record is private; a simple idea is adding noise to each record to protect private information. However, if this privacy preservation method is directly applied to big data, the introduced noise will significantly increase so that data utility dramatically drops.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis

Yang

Bai

2019

Security and Communication Networks

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With the widespread application of big data, privacy-preserving data analysis has become a topic of increasing significance. The current research studies mainly focus on privacy-preserving classification and regression. However, principal component analysis (PCA) is also an effective data analysis method which can be used to reduce the data dimensionality, commonly used in data processing, machine learning, and data mining. In order to implement approximate PCA while preserving data privacy, we apply the Laplace mechanism to propose two differential privacy principal component analysis algorithms: Laplace input perturbation (LIP) and Laplace output perturbation (LOP). We evaluate the performance of LIP and LOP in terms of noise magnitude and approximation error theoretically and experimentally. In addition, we explore the variation of performance of the two algorithms with different parameters such as number of samples, target dimension, and privacy parameter. Theoretical and experimental results show that algorithm LIP adds less noise and has lower approximation error than LOP. To verify the effectiveness of algorithm LIP, we compare our LIP with other algorithms. The experimental results show that algorithm LIP can provide strong privacy guarantee and good data utility.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis

Yang

Bai

2019

Security and Communication Networks

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“…The last equation uses the fact that p 1· + p 2· = 1. Note that the plus-minus sign of ρ 12 is equivalent to the sign of Cov(x 1 , x 2 ), 15 then we prove the left half of Eq. (28) by setting x 1,2 > x 1,1 as usual.…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 53%

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

Ren

Yang

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closedform expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

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“…Perturbation based methods include link modification strategy and randomization strategy, in which the former proposes link addition and deletion mechanism to meet the desired constrains, such as kdegree anonymity [40], and k-automorphism anonymity [41]; the latter attempts to change network structure by randomly adding and removing links. In addition, differential privacy methods [42], [43] are also proposed for network data anonymization.…”

Section: Related Work a Network Privacy Preservationmentioning

confidence: 99%

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Xian

Qiao

et al. 2020

IEEE Access

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Social networks are extensively exploited by third-party consumers such as researchers and advertisers to understand user characteristics and behaviors. In general, before network data is published, sensitive relationships should be anonymized to prevent the compromise of individual privacy. To quantify the guarantee level of privacy-preserving mechanisms and mitigate users' privacy concerns, numerous studies concerning network data de-anonymization have been carried out. However, most existing studies focus on single-view data, and privacy protection for multi-view data that is ubiquitous in the era of big data has not been yet extensively explored. In this study, we are interested in answering the following question: Are the traditional privacy protection methods still valid for the anonymization of multi-view data? In this study, we propose a Multi-View Low-Rank Coding (MVLRC) based network data de-anonymization framework to assess the vulnerability of privacy protection techniques by accurately reconstructing a large portion of the original data. Specifically, the framework assumes that in principle, the target and auxiliary networks have common structural patterns, and they can be modeled together to infer the hidden structure of the target network. The essential components of our work include the following: (1) a robust network representation model for structural pattern learning; (2) the network representation based multiview modeling of target network and auxiliary network; (3) the inference of the anonymized links via target network reconstruction. Experimental results on synthetic networks and three real-world networks demonstrate that auxiliary networks can be utilized by malicious adversaries for privacy inference attacks. Thus, the privacy protection of multi-view network data needs more sophisticated anonymization techniques. INDEX TERMS Privacy preserving, de-anonymization, network data, multi-view learning, low-rank coding.

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Differentially Private Data Publishing and Analysis: A Survey

Cited by 264 publications

References 89 publications

Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis

Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

Multi-View Low-Rank Coding-Based Network Data De-Anonymization

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