Publishing a database instance containing individual information poses two kinds of privacy risk: presence leakage, by which the attackers can explicitly identify individuals in (or not in) the database, and association leakage, by which the attackers can unambiguously associate individuals with sensitive information. However, the existing privacy-preserving data publishing techniques that can ...

In this paper, we present an accurate and efficient privacy preserving data mining technique in Electronic Health Record (EHR) by using k –anonymity and decision tree C4.5 that is useful to generate pattern for medical research or any clinical trials. It is analyzed that anonymization offers better privacy rather than other privacy preserving method like that randomization, cryptography, pertur...

Data services have almost become a standard way for data publishing and sharing on top of the Web. In this paper, we present a secure and privacy-preserving execution model for data services. Our model controls the information returned during service execution based on the identity of the data consumer and the purpose of the invocation. We implemented and evaluated the proposed model in the hea...

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-...

Big data refers to a massive volume of collected from heterogeneous sources including Internet Things (IoT) devices. analytics is playing crucial role in extracting patterns that would benefit efficient and effective decision making. Processing this poses several critical issues such as scalability, security privacy. To preserve privacy, numerous privacy-preserving mining publishing techniques ...

This paper investigates the problem of suppressing access to sensitive linkage information over data published by users of an online social network service. We unveil the potential threats by inferring linkage information from the user-published data, and suggest a class of data publishing schemes to enable distributed data publication by individual users but hide the sensitive information. Our...

k-Anonymity is one of the most popular conventional techniques for protecting the privacy of an individual. The shortcomings in the process of achieving k-Anonymity are presented and addressed by using Coalitional Game Theory (CGT) [1] and Concept Hierarchy Tree (CHT). The existing system considers information loss as a control parameter and provides anonymity level (k) as output. This paper pr...

Article history: Received 27 September 2010 Received in revised form 10 April 2011 Accepted 5 July 2011 Available online 22 July 2011 Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building classification mode...