With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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A Comprehensive Survey on Local Differential Privacy

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) has been 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. What's more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis in Crowdsensing.

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.

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

Social network platform is the one where a huge number of information from social networks are published and accessed by third parties or with advertising partners for better social targeting. Henc...

A Review on Privacy Preservation of Social Networks Using Graphs

Clustering by fast search and find of density peaks (CFSFDP) is an efficient algorithm for density-based clustering. However, such algorithm inevitably results in privacy leakage. In this paper, we propose DP-CFSFDP to address this problem with differential privacy, which adds random noise in order to distort the data but preserve its statistical properties. Besides, due to the poor performance of CFSFDP on evenly distributed data, we further optimize the clustering process with reachable-centers and propose DP-rcCFSFDP. The experimental results show that, under the same privacy budget, DP-rcCFSFDP can improve the clustering effectiveness while preserving data privacy compared with DP-CFSFDP.

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Density Peak Clustering Algorithm Based on Differential Privacy Preserving

With the rapid development of big data technology, the issue of preserving personal privacy has attracted more and more attention. It has been shown that protecting the published graph with the differential privacy can ensure not only the quantitative privacy, but also the data usability. In this paper, we first put forward an optimized randomized response algorithm for generating synthetic graph, which dose not depend on a trusted third party in charge of collecting data. Furthermore, based on multi-party computation clustering, we propose a generated graph model under local differential privacy (LDPGM). The experiment indicates that LDPGM can effectively control the density of the synthetic graph so that significantly reduce the error between the synthetic graph and the original graph. Therefore, it maintains the properties of the original graph well and ensures high usability.

A Two-Phase Algorithm for Generating Synthetic Graph Under Local Differential Privacy

With the widespread use of new information systems such as social networks, recommendation systems as well as location-based services, graph data has become a very common and important data type. It has been shown that, from these collected graph data, some special substructures can be found through clustering analysis, and can further support the intelligent decision. However, directly publishing or using clustering results on these graph data would disclose the privacy information of system users. To this end, based a classical structural clustering algorithm for networks (SCAN) and the technology of differential privacy, we propose a differentially private graph clustering algorithm named DP-SCAN. Specifically, we first reasonably calibrate the global sensitivity of the function of computing structure similarity between nodes in the graph, and thus specify parameters of the Laplace mechanism for capturing differential privacy. Then, we provide details of the DP-SCAN algorithm. The theoretical analysis indicates that DPSCAN algorithm satisfies e-differential privacy, without trading off the clustering efficiency. The experimental results show that, when compared with the original SCAN clustering algorithm, DP-SCAN clustering algorithm can maintain the validity of clustering under the premise of satisfying differential privacy.

Differentially Private Graph Clustering Algorithm Based on Structure Similarity

Let G be a ﬁnite group. A subgroup L of G is said to be weakly-supplemented in G if there exists a proper subgroup M of G such that G = LM . The key of this paper is to determine the supersolvability and p -nilpotency of ﬁnite groups under assumption that some primary subgroups are weakly-supplemented.

The influence of weakly-supplemented subgroups on the structure of finite groups

The invention relates to the privacy protection technical field of graph data publishing, in particular to a degree histogram publishing method of a graph under point difference privacy Firstly, theinvention utilizes the edge removal method SER based on degree sorting to reduce the global sensitivity in the publishing mechanism by projecting the original image onto a compressed image Then, based on the SER projection method, a degree histogram publishing method which satisfies the point difference privacy is proposed Simulation results show that, compared with the existing methods, the SERprojection method can preserve the edge information of the original graph to the maximum extent under the same constraint conditions, which lays a good foundation for the subsequent data processingCompared with the existing degree distribution publishing method, the degree histogram publishing method of the vertex difference privacy lower graph based on the SER projection method has advantagesin L1 error and KS distance, so that the degree distribution after publishing is closer to the degree distribution of the original graph, and the usability is higher

Yuxuan Zhang

Papers

A Two-Phase Algorithm for Generating Synthetic Graph Under Local Differential Privacy

Differentially Private Graph Clustering Algorithm Based on Structure Similarity

The influence of weakly-supplemented subgroups on the structure of finite groups

Degree histogram publishing method of graph under point difference privacy