A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Xie, Yuqin; Zheng, Mingchun

doi:10.3390/a9040085

Cited by 6 publications

(3 citation statements)

References 14 publications

(22 reference statements)

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“…Thus, deep learning platforms have been designed for mining values in big data [27]. A future direction is to design above methods and experiments in big data environments [28][29][30][31] and investigate their scalability and running time performance under different datasets and parameters k, K combinations.…”

Section: Discussionmentioning

confidence: 99%

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

et al. 2018

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In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data. The k-anonymity set consists of the k nearest neighbors. We constructed a motion vector of the form (x,y,g,v) where x and y are the spatial coordinates, g is the angle direction, and v is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only k-Nearest Neighbor (k-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with k-NN. Actually, it applied k-NN inside a cluster of mobile users with similar motion pattern (g,v). We defined a metric, called vulnerability, that measures the rate at which k-NNs are varying. This metric varies from 1 k (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to 1 k in clustering method, which means that the k-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

et al. 2018

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“…Differential privacy (DP) [41] is a well-known state of the art method for protecting user's privacy in interactive setting (i.e., in this setting data owner provides an interface for receiving queries and answer queries by respecting user's privacy). Xie et al [42] proposed a differentiated k-anonymity ℓ-diversity social network anonymity algorithm. The main goals of the presented algorithm are to protect user's privacy and enhance the anonymous data utility.…”

Section: Background and Related Workmentioning

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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“…In this paper we have seen that the data of the Caller ID applications contains private information of individuals, so the question is how to prevent individual privacy disclosure while publishing this data. The ongoing and future work will consider anonymization and privacy preserving techniques along the lines [14] and [25].…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Preserving Privacy in Caller ID Applications

Stefanović¹,

Ghilezan²

2021

IFIP Advances in Information and Communication Technology

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Caller identification (or Caller ID) is a telephone service that transmits a caller's phone number to a receiving party's telephony equipment when the call is being set up. Besides the telephone number, the Caller ID service may transmit a name associated with the calling telephone number. The appearance of the first Caller ID devices caused suspicion among the users and the public because of the potential security and privacy issues that the caller identification may cause. Privacy issues apply to users of the Caller ID applications, but also to non-users whose phone numbers are stored in the database of some Caller ID application. The emergence of the data privacy laws has led discussions on the privacy policies of Caller ID applications and their compliance with the law. In this paper we investigate two Caller ID applications, Truecaller and Everybody, and compliance of their privacy policies with the data privacy laws, especially the GDPR, the ePrivacy Directive and the ePrivacy Regulation. Further, we deal in more detail with the data privacy problem of non-users and we give the connection between those problems, and the inverse privacy problem. In order to solve the privacy problem of non-users, we develop the mathematical model based on the notions of privacy variables and sensitivity function. Finally, we discussed open questions related to the identity protection of Caller ID app users and non-users, and their trust in Caller ID apps.

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A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Cited by 6 publications

References 14 publications

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

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

Preserving Privacy in Caller ID Applications

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