Local differential privacy for social network publishing

Liu, Peng; Xu, Yuanxin; Jiang, Quan; Tang, Yuwei; Guo, Yameng; Wang, Lie; Li, Xianxian

doi:10.1016/j.neucom.2018.11.104

Cited by 39 publications

(20 citation statements)

References 9 publications

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“…k-anonymity [10], ℓ-diversity [11], tcloseness [12], slicing [13], anatomy [14], and their improved versions are well known relational anonymization techniques. Random walk [15], differential privacy [16], cluster based techniques [17], k-anonymity based techniques [18,19], edge editing [20], k-degree anonymization [21], k-neighbourhood anonymization [22], k-isomorphism anonymization [23], kautomorphism anonymization [24], edge differential privacy [25], node differential privacy [26], vertex degree distribution and attribute value distribution [27,28], and uncertainty semantics [29] are the most widely used structural anonymization techniques.…”

Section: Introductionmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls overgeneralization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Section: Introductionmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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“…Zhang et al [ 188 ] adopted the idea of multi-party computation clustering to generate a graph model under the optimized RR algorithm. Besides, Liu et al [ 189 ] used the perturbed local communities to generate a synthetic network that maintains the original structural information.…”

Section: Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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“…where x i ∈ R 100 is an input vector, x i ∼ N (0, Σ), covariance matrix Σ = 0.5 |i−j| , 1 ≤ i, j ≤ n, coefficient vector w ∈ R 100 , where w(1 : 8) = (10,9,8,7,6,5,4, 0.5) , and w(9 : 100) = (0, 0, ..., 0). is standardized normal random error, and sample number n. If p(y i = 1|x i ) ≥ 0.5, let y i = 1, else, y i = −1.…”

Section: Simulationmentioning

confidence: 99%

“…As a privacy-preserving technology with rigorous mathematical theory, differential privacy is suitable for privacy preserving in the era of big data, and has a broad application [8][9][10][11]. In the framework of differential privacy, the degree of privacy information leakage is controlled by the parameter .…”

Section: Introductionmentioning

confidence: 99%

Differential privacy for sparse classification learning

Wang

Zhang

2020

Neurocomputing

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In this paper, we present a differential privacy version of convex and nonconvex sparse classification approach. Based on alternating direction method of multiplier (ADMM) algorithm, we transform the solving of sparse problem into the multistep iteration process. Then we add exponential noise to stable steps to achieve privacy protection. By the property of the post-processing holding of differential privacy, the proposed approach satisfies the −differential privacy even when the original problem is unstable. Furthermore, we present the theoretical privacy bound of the differential privacy classification algorithm. Specifically, the privacy bound of our algorithm is controlled by the algorithm iteration number, the privacy parameter, the parameter of loss function, ADMM pre-selected parameter, and the data size. Finally we apply our framework to logistic regression with L1 regularizer and logistic regression with L 1/2 regularizer. Numerical studies demonstrate that our method is both effective and efficient which performs well in sensitive data analysis.

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Local differential privacy for social network publishing

Cited by 39 publications

References 9 publications

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Differential privacy for sparse classification learning

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