Narrow Results

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 $K$-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 $K$-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 $K$-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.

With the rapid development of cloud computing and the popularization of mobile equipment, location-based cloud services (LBCS) are widely used, bringing great convenience to human industrial production and daily life. However, since LBCS is a location-based service, how to protect personal privacy when receiving the service is a matter of widespread concern for cloud users. To solve this problem, this paper introduces a new privacy protection method based on K-anonymity, which not only protects the cloud user’s query privacy as well as location privacy but also resolves the contradiction between privacy protection and service quality. From the simulation experimental results, we verify the effectiveness of our proposed method.

Due to the proliferation of online social networking, a large number of personal data are publicly available. As such, personal attacks, reputational, financial, or family losses might occur once this personal and sensitive information falls into the hands of malicious hackers. Research on Privacy-Preserving Network Publishing has attracted much attention in recent years. But most work focus on node de-identification and link protection. In academic social networks, business transaction networks, and transportation networks, etc, node identities and link structures are public knowledge but weights and shortest paths are sensitive. In this work, we study the problem of k-anonymous path privacy. A published network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices [21]. In order to achieve such privacy, three different strategies of modification on edge weights of directed graphs are proposed. Numerical comparisons show that weight-proportional-based strategy is more efficient than PageRank-based and degree-based strategies. In addition, it is also more efficient and causes less information loss than running on un-directed graphs.

In data privacy, the evaluation of the disclosure risk has to take into account the fact that several releases of the same or similar information about a population are common. In this paper we discuss this issue within the scope of k-anonymity. We also show how this issue is related to the publication of privacy protected databases that consist of linked tables. We present algorithms for the implementation of k-anonymity for this type of data.

Re-identification and record linkage are tools used to measure disclosure risk in data privacy. Given two data files, record linkage establishes links between those records that correspond to the same individual. These links are often expressed in terms of probability distributions.

This paper presents a review of a formalization of re-identification in terms of compatible belief functions. This formalization makes it possible to define the set of methods for re-identification that are relevant for the estimation of disclosure risk in privacy protection. Any re-identification method that does not fulfill the criteria for being in this set, may be discarded in a theoretical disclosure risk analysis.

The focus in this paper is on providing examples of how this formalization can be applied in a few different scenarios in data privacy

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer (Genetic GWO) algorithm for which a C-mixture parameter is used. The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints, such as the $k$-anonymity, $l$-diversity and the $m$-privacy. A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size. The minimum value of the fitness ensures the maximum utility and the maximum privacy. Experimentation was carried out using the adult dataset, and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9, respectively.

k-anonymity is a widely used technology for privacy protection. In order to prevent attackers from digging out private information based on data published and his(her) background knowledge, a k-anonymity method is proposed, which uses the idea of clustering partition. Two different types of quasi-identifier attributes are individually subjected to a generalization process. The distance between tuples as well as the distance between a tuple and an equivalence class is also defined. In the process of clustering partition, some tuples are selected to constitute equivalence classes one by one, according to the principle of minimum distance value. k-anonymization is then completed, finally, by a separate generalization process. Through experiments comparing the performance of our method with that of an existing anonymization algorithm, EBKC, the effectiveness of the approach is verified.