Non-cryptographic Approaches for Collaborative Social Network Data Publishing - A Survey

Social network data has been increasingly made publicly available and analyzed in a wide spectrum of application domains. The practice of publishing social network data has brought privacy concerns to the front. Serious concerns on privacy protection in social networks have been raised in recent years. Realization of the promise of social networks data requires addressing these concerns. This paper considers the privacy disclosure in social network data publishing. In this paper, we present a systematic analysis of the various risks to privacy in publishing of social network data. We identify various attacks that can be used to reveal private information from social network data. This information is useful for developing practical countermeasures against the privacy attacks.KeywordsPrivacy disclosureSocial networksThreat analysisData publications

Privacy-preserving data publishing is a mechanism for sharing data while ensuring the privacy of individuals is preserved in the published data and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health research. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this thesis, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.

Social network data publishing is dynamic, and attackers can perform association attacks based on social network directed graph data at different times. The existing social network privacy protection technology has low performance in dealing with large-scale dynamic social network directed graph data, and anonymous data publishing does not meet the needs of community structure analysis. A Dynamic Social Network Directed Graph K-In&Out-Degree Anonymity (DSNDG-KIODA) method to protect community structure is proposed. The method is based on the dynamic grouping anonymity rule to anonymize the dynamic K-in&out-degree sequence, and the virtual node distribution is added in parallel to construct an anonymous graph. The node information is transmitted based on the GraphX, and the virtual node pairs are selected and deleted according to the change of the directed graph modularity to reduce information loss. The experimental results show that the DSNDG-KIODA method improves the efficiency of processing large-scale dynamic social network directed graph data, and ensures the availability of community structure analysis when data is released.

Optimized key generation-based privacy preserving data mining model for secure data publishing

As growing interest in data publishing and analysis, privacy preserving data publication has become more important today. When a table containing the sensitive information is published, privacy of each individual should be protected. On the other hand, a data holder also considers minimizing information loss for analysis as long as the privacy is preserved. A few years ago, k-anonymity and l-diversity models have been suggested in order to protect privacy. However, these solutions are limited to static data release. Recently, the m-invariance model has been proposed to apply publication of dynamic environments. However, m-invariance generalization technique causes high information loss. In this paper, we propose a simple and safe anonymization technique without generalization while assuring high data utility in dynamic environments.

Data privacy encompasses the safeguarding and control individuals exercise over their personal information and data. It revolves around ensuring the confidentiality and security of sensitive data, including financial records, health information, and unique identifiers. In the era of extensive data collection, storage, and sharing in the digital landscape, preserving data privacy has become imperative to uphold individuals' rights and shield them from potential harm. From artificial intelligence and machine learning to internet of things (IoT) devices, innovative solutions have transformed our way of life and work. However, every innovation brings the responsibility to safeguard the privacy and security of individuals whose data is collected and processed. So, balancing innovation and personal security is a nuanced task. While innovation offers substantial benefits, it also poses risks to personal privacy without adequate regulation. This chapter dives into the diverse exploration of human rights protection concerning data and privacy of individuals in the digital arena.

<p> Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The τ -safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new τ -safe (l, k)-diversity privacy model based on generalization and segmentation by record anonymity satisfying l-diversity and individual anonymity satisfying k-anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the m-invariance and τ -safety model in the sequential publication with arbitrary updates. </p>

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The $\tau $ -safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new $\tau $ -safe ( $l,k$ )-diversity privacy model based on generalization and segmentation by record anonymity satisfying $l$ -diversity and individual anonymity satisfying $k$ -anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the $m$ -invariance and $\tau $ -safety model in the sequential publication with arbitrary updates.

In recent years, social network research has advanced significantly. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of social network analysis and mining in academia, politics, homeland security and business. However, growing popularity of on-line social networking and data publishing, not only brings the convenience of information sharing but also concerns of privacy breaches, as sensitive information, through links and inferences, individual's identity, health, financial status, political affiliations, etc, might be disclosed. In order to preserve privacy of users, anonymization is required prior to attempts to make the data more widely available to public. In this talk, we will concentrate on introducing recent studies in privacy preservation in data publishing, data mining, networking publishing, location publishing, and mobile publishing.

5 Chapter - SRS: An integration platform for databanks and analysis tools in bioinformatics

Chapter 10 - Techniques for Privacy Preserving Data Publication in the Cloud for Smart City Applications

Huge amount of personal data is collected by online applications and its protection based on privacy has brought a lot of major challenging issues. Hence, the [Formula: see text]-anonymization with privacy-preserving data publishing has emerged as an active research field. The published data contains personalized information, which may be used for analysis converting it to useful information. In this paper, Quasi identifier (QI) data publishing with data preservation through the [Formula: see text]-anonymization process is proposed. Moreover, the risks such as the temporal attack in the previous release of re-identifying QI information are evaluated using the [Formula: see text]-anonymity model. The development of independent and ensemble classifiers for finding efficient QI’s to avoid temporal attacks is the major objective of this paper. Therefore, the classifiers like Naïve Bayes, Support Vector Machine, and Multilayer Perceptron are used as base classifiers. An ensemble model based on these base classifiers is also used. The experimental results demonstrate that, the proposed classification approach is an effective K-anonymity tool for the enhancement of sequential release.

Privacy Preservation in Data Mining for Health Care

Now a day’s Data publishing process is the compulsory for visualizing the data sets to other parties. But in publishing process there is thread for data set owners by disclosing the sensitive information. For avoiding those problems we using anonymization techniques for secure data publishing. Most Anonymization techniques such as Generalization and Bucketization are using now. But these anonymization techniques have some limitation. Generalization for k-anonymity losses considerable amount of information for high- dimensional data and Bucketization does not prevent membership disclosure, because bucketization publishes the QI values in their original forms, It requires a clear separation between QI’s and SA’s, but in many data sets, it is unclear which attributes are QI’s and which are SA’s and it breaks the attributes correlations between the QI’s and the SA’s. This paper introduces new anonymization technique slicing to overcome all the drawbacks of bucketization and generalization Keywords: - Sensitive information, High dimensional data, data anonymization, data publishing, data security, generalization and bucketization.

The widespread adoption of Linked Data has been driven by the increasing demand for information exchange between organisations, as well as by data publishing regulations in domains such as health care and governance. In this setting, sensitive information is at risk of disclosure since published data can be linked with arbitrary external data sources. In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of safe and optimal anonymisations. Safety ensures that the anonymised data can be published with provable protection guarantees against linking attacks, whereas optimality ensures that it preserves as much information from the original data as possible, while satisfying the safety requirement. We establish the complexity of the underpinning decision problems both under open-world semantics inherent to RDF and a closed-world semantics, where we assume that an attacker has complete knowledge over some part of the original data.