P-sensitive K-anonymity Research Articles

Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

(p+, α, t)-Anonymity Technique Against Privacy Attacks

Privacy preservation is a major concern in current technology where enormous amounts of data are being collected and published for carrying out analysis. These data may contain sensitive information related to individual who owns them. If the data is published in their original form, they may lead to privacy disclosure which threats privacy requirements. Hence, the data should be anonymized before publishing so that it becomes challenging for intruders to obtain sensitive information by means of any privacy attack model. There are popular data anonymization techniques such as k-anonymity, l-diversity, p-sensitive k-anonymity, (l, m, d) anonymity, and t-closeness, which are vulnerable to different privacy attacks discussed in this paper. The proposed technique called (p+, α, t)-anonymity aims to anonymize the data in such a way that even though intruder has sufficient background knowledge on the target individual he will not be able to infer anything and breach private information. The anonymized data also provide sufficient data utility by allowing various data analytics to be performed.

A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes

Privacy preserving data publishing of electronic health record (EHRs) for 1 to M datasets with multiple sensitive attributes (MSAs) is an interesting and challenging issue. There is always a trade-off between privacy and utility in data publishing. Most of the privacy-preserving models shows critical privacy disclosure issues and, hence, they are not robust in practical datasets. The k-anonymity model is a broadly used privacy model to analyze privacy disclosures, however, this model is only useful against identity disclosure. To address the limitations of k-anonymity, a group of privacy model extensions have been proposed in past years. It includes a p-sensitive k-anonymity model, a p+-sensitive k-anonymity model, and a balanced p+-sensitive k-anonymity model. However these privacy-preserving models are not sufficient to preserve the privacy of end-users in practical datasets. In this paper we have formalize the behavior of an adversary which perform identity and attribute disclosures on balanced p+-sensitive k-anonymity model with the help of adversarial scenarios. Since balanced p+-sensitive k-anonymity model is not sufficient for 1 to M with MSAs datasets privacy preservation. We propose an extended privacy model called “1: M MSA-(p, l)-diversity” for 1: M dataset with MSAs. We then perform formal modeling and verification of the proposed model using High-Level Petri Nets (HLPN) to confirm privacy attacks invalidation. Experimental results show that our proposed “1: M MSA-(p, l)-diversity model” is efficient and provide enhanced data utility of published data.

Security Enhancement and Privacy Preserving Of Big Data

Big data is gaining the popularity among the data scientist and the people from the Biology related disciplines. Big Data is very huge in volume and comes very fast from various sources. Millions of tweets or posts are generated per second on social networking sites. Big data has many issues of i.e. nature, storing, management, and processing, privacy and security in disclosing of attributes of the sensitive data in data of healthcare etc. For maintaining the privacy there are k-anonymity, p-sensitive k-anonymity, l-diversity, t-closeness, and k-concealment ways. In this paper an anonymity algorithm is proposed which will be used to enhance the security and privacy preserving of sensitive attributes of big data.

An efficient privacy mechanism for electronic health records

Electronic health records (EHRs), digitization of patients' health record, offer many advantages over traditional ways of keeping patients' records, such as easing data management and facilitating quick access and real-time treatment. EHRs are a rich source of information for research (e.g. in data analytics), but there is a risk that the published data (or its leakage) can compromise patient privacy. The k-anonymity model is a widely used privacy model to study privacy breaches, but this model only studies privacy against identity disclosure. Other extensions to mitigate existing limitations in k-anonymity model include p-sensitive k-anonymity model, p+-sensitive k-anonymity model, and (p, α)-sensitive k-anonymity model. In this paper, we point out that these existing models are inadequate in preserving the privacy of end users. Specifically, we identify situations where p+-sensitive k-anonymity model is unable to preserve the privacy of individuals when an adversary can identify similarities among the categories of sensitive values. We term such attack as Categorical Similarity Attack (CSA). Thus, we propose a balanced p+-sensitive k-anonymity model, as an extension of the p+-sensitive k-anonymity model. We then formally analyze the proposed model using High-Level Petri Nets (HLPN) and verify its properties using SMT-lib and Z3 solver. We then evaluate the utility of release data using standard metrics and show that our model outperforms its counterparts in terms of privacy vs. utility tradeoff.

ABAKA: A novel attribute-based k-anonymous collaborative solution for LBSs

The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major concern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s profile attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p-sensitive k-anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal.

Extended K-Anonymity Model for Privacy Preserving on Micro Data

Today, information collectors, particularly statistical organizations, are faced with two conflicting issues. On one hand, according to their natural responsibilities and the increasing demand for the collected data, they are committed to propagate the information more extensively and with higher quality and on the other hand, due to the public concern about the privacy of personal information and the legal responsibility of these organizations in protecting the private information of their users, they should guarantee that while providing all the information to the population, the privacy is reasonably preserved. This issue becomes more crucial when the datasets published by data mining methods are at risk of attribute and identity disclosure attacks. In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed. The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio. In order to eliminate these drawbacks, this paper proposes an algorithm that fully protects the propagated micro data against identity and attribute disclosure and significantly reduces the distortion ratio during the anonymity process. Index Terms—Privacy preservation, data mining, k- anonymity, micro data.

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Privacy preserving data mining (PPDM) has become more and more important because it allows sharing of privacy sensitive data for analytical purposes. A big number of privacy techniques were developed most of which used the k-anonymity property which have many shortcomings, so other privacy techniques were introduced (l-diversity, p-sensitive k-anonymity, (α, k)-anonymity, t-closeness, etc.). While they are different in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data. This paper is concerned with providing an enhanced privacy technique that combines some anonymity techniques to maintain both privacy and data utility by considering the sensitivity values of attributes in queries using sensitivity weights which determine taking in account utility-based anonymization and then only queries having sensitive attributes whose values exceed threshold are to be changed using generalization boundaries. The threshold value is calculated depending on the different weights assigned to individual attributes which take into account the utility of each attribute and those particular attributes whose total weights exceed the threshold values is changed using generalization boundaries and the other queries can be directly published. Experiment results using UT dallas anonymization toolbox on real data set adult database from the UC machine learning repository show that although the proposed technique preserves privacy, it also can maintain the utility of the publishing data.

Privacy Preservation for Knowledge Discovery: A Survey

Today's globally networked society places great demand on the dissemination and sharing of information. Privacy Preservation is an important issue in the release of data for mining purposes. How to efficiently protect individual privacy in data publishing is especially critical. With releasing of microdata such as social security number disease by some organization should contain privacy in data publishing. Data holders can remove explicit identifiers to gain privacy but other attributes which are in published data can lead to reveal privacy to adversary. So several methods such as K-anonymity, L-diversity, T-closeness, (n,t) closeness, (α,k)-anonymization, p-sensitive k-anonymity and others method come into existence to maintain privacy in data publishing.

AN OVERVIEW OF p-SENSITIVE k-ANONYMITY MODELS FOR MICRODATA ANONYMIZATION

In this paper, we present an overview of p-sensitive k-anonymity models including the basic model, the extended p-sensitive k-anonymity, the constrained p-sensitive k-anonymity, and the (p+, α)-sensitive k-anonymity. Existing properties of these models are reviewed and illustrated, and new properties regarding the maximum number of QI-clusters are discussed and proved. This paper includes a review of related anonymity models and a very brief summary of existing algorithms for the family of p-sensitive k-anonymity models.

P-Sensitive K-Anonymity with Generalization Constraints

Numerous privacy models based on the k-anonymity property and extending the k-anonymity model have been introduced in the last few years in data privacy research: l-diversity, p-sensitive k-anonymity, (α, k) anonymity, t-closeness, etc. While differing in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data as a whole. We consider a new approach, where requirements on the amount of distortion allowed on the initial data are imposed in order to preserve its usefulness. Our approach consists of specifying quasiidentifiers' generalization constraints, and achieving p-sensitive k-anonymity within the imposed constraints. We think that limiting the amount of allowed generalization when masking microdata is indispensable for real life datasets and applications. In this paper, the constrained p-sensitive k-anonymity model is introduced and an algorithm for generating constrained p-sensitive k-anonymous microdata is presented. Our experiments have shown that the proposed algorithm is comparable with existing algorithms used for generating p-sensitive k-anonymity with respect to the results' quality, and obviously the obtained masked microdata complies with the generalization constraints as indicated by the user.

Extended k-anonymity models against sensitive attribute disclosure

p-Sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a set of quasi-identifier attributes. In this paper, we identify the situations when the p-sensitive k-anonymity property is not enough for the sensitive attributes protection. To overcome the shortcoming of the p-sensitive k-anonymity principle, we propose two new enhanced privacy requirements, namely p + -sensitive k-anonymity and ( p , α ) -sensitive k-anonymity properties. These two new introduced models target at different perspectives. Instead of focusing on the specific values of sensitive attributes, p + -sensitive k-anonymity model concerns more about the categories that the values belong to. Although ( p , α ) -sensitive k-anonymity model still put the point on the specific values, it includes an ordinal metric system to measure how much the specific sensitive attribute values contribute to each QI-group. We make a thorough theoretical analysis of hardness in computing the data set that satisfies either p + -sensitive k-anonymity or ( p , α ) -sensitive k-anonymity. We devise a set of algorithms using the idea of top-down specification, which is clearly illustrated in the paper. We implement our algorithms on two real-world data sets and show in the comprehensive experimental evaluations that the two new introduced models are superior to the previous method in terms of effectiveness and efficiency.

Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing

Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the k-anonymity model recently. In this paper, we propose two new privacy protection models called (p, α)-sensitive k-anonymity and (p+, α)-sensitive k-anonymity, respectively. Different from previous the p-sensitive k-anonymity model, these new introduced models allow us to release a lot more information without compromising privacy. Moreover, we prove that the (p, α)-sensitive and (p+, α)-sensitive k-anonymity problems are NP-hard. We also include testing and heuristic generating algorithms to generate desired micro data table. Experimental results show that our introduced model could significantly reduce the privacy breach.

A retrospective analysis of lingual nerve injuries following sagittal osteotomies

Electronic health records (EHRs), digitization of patients' health record, offer many advantages over traditional ways of keeping patients' records, such as easing data management and facilitating quick access and real-time treatment. EHRs are a rich source of information for research (e.g. in data analytics), but there is a risk that the published data (or its leakage) can compromise patient privacy. The k-anonymity model is a widely used privacy model to study privacy breaches, but this model only studies privacy against identity disclosure. Other extensions to mitigate existing limitations in k-anonymity model include p-sensitive k-anonymity model, p+-sensitive k-anonymity model, and (p, α)-sensitive k-anonymity model. In this paper, we point out that these existing models are inadequate in preserving the privacy of end users. Specifically, we identify situations where p+-sensitive k-anonymity model is unable to preserve the privacy of individuals when an adversary can identify similarities among the categories of sensitive values. We term such attack as Categorical Similarity Attack (CSA). Thus, we propose a balanced p+-sensitive k-anonymity model, as an extension of the p+-sensitive k-anonymity model. We then formally analyze the proposed model using High-Level Petri Nets (HLPN) and verify its properties using SMT-lib and Z3 solver. We then evaluate the utility of release data using standard metrics and show that our model outperforms its counterparts in terms of privacy vs. utility tradeoff.