Secure Login of Statistical Data With Two Parties

Privacy-containing data publishing shows the problem of releasing sensitive data while the mining of useful information. Among present privacy models, SHA privacy algorithm provides more security and privacy model. In this paper, we address the problem of released private data, where different dataset for the same set of user are held by two parties. Here, we present an algorithm for sensitive private data released on web in the form of statistical data. After this, we propose a SHA algorithm that releases differentially private data in a secure way during the privacy computation. Experimental results on real-scenario suggest that the proposed algorithm can effectively preserve information during mining of private information. DOI: 10.17762/ijritcc2321-8169.150312

The Role of Quasi-identifiers in k-Anonymity Revisited

The concept of k-anonymity, used in the recent literature to formally evaluate the privacy preservation of published tables, was introduced based on the notion of quasi-identifiers (or QI for short). The process of obtaining k-anonymity for a given private table is first to recognize the QIs in the table, and then to anonymize the QI values, the latter being called k-anonymization. While k-anonymization is usually rigorously validated by the authors, the definition of QI remains mostly informal, and different authors seem to have different interpretations of the concept of QI. The purpose of this paper is to provide a formal underpinning of QI and examine the correctness and incorrectness of various interpretations of QI in our formal framework. We observe that in cases where the concept has been used correctly, its application has been conservative; this note provides a formal understanding of the conservative nature in such cases.Comment: 17 pages. Submitted for publicatio

A look ahead approach to secure multi-party protocols

Secure multi-party protocols have been proposed to enable non-colluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. Therefore, the high overhead makes it necessary for parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look ahead approach, specifically for secure multi-party protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. Look ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods