An efficient secure sum of multi-scalar products protocol base on elliptic curve

The secure scalar product protocol is widely applied to solve practical problems such as privacy-preserving data mining, secure auction, secure electronic voting, privacy-preserving recommendation system, privacy-preserving statistical data analysis, etc.. In this paper, we propose an efficient multi-party secure computation protocol using Elliptic curve cryptography, which allows to compute the sum value of multi-scalar products without revealing about the input vectors. Moreover, theoretical and experimental analysis shows that the proposed method is more efficient than others in both computation and communication.

Recent Developments in Privacy-preserving Mining of Clinical Data

With the dramatic improvements in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but also open up possibilities for identification and abuse of personal information. In this article, we survey recent research on classical methods of privacy-preserving data mining. Looking at dominant techniques and recent innovations to them, we examine the applicability of these methods to the privacy-preserving analysis of clinical data. We also discuss promising directions for future research in this area.

Privacy Preserving Data Mining

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.

Efficient Model for Privacy Preserving Classification Of Data Streams

Privacy preserving data mining has become progressively mainstream since it permits sharing of privacy delicate data for examination purposes .So individuals have gotten progressively reluctant to share their data, regularly bringing about people either declining to share their data or giving inaccurate data. As of late, privacy preserving data mining has been concentrated broadly, on account of the wide multiplication of delicate data on the web. Data Mining manages programmed extraction of already obscure examples from a lot of data sets. These data sets ordinarily contain touchy individual data or basic business data, which thusly get presented to different gatherings during Data Mining exercises. This makes hindrance in Data Mining measure. Answer for this issue is given by Privacy preserving in data mining (PPDM). PPDM is a specific arrangement of Data Mining exercises where procedures are developed to secure privacy of the data, so the information revelation cycle can be completed without obstruction. The target of PPDM is to shield delicate data from spilling in the mining cycle alongside exact Data Mining results. The objective of this paper is to introduce the survey on different privacy preserving strategies which are useful in mining huge measure of data with sensible productivity and security.

DATA PROCESSING THROUGH AN ADDITIVE ROTATIONAL PERTURBATION TECHNIQUE IN A SECURED ENVIRONMENT OF PPRIVACY

As the usage of internet and web applications emerges faster, security and privacy of the data is the most challenging issue which we are facing, leading to the possibility of being easily damaged. Various conventional techniques are used for privacy preservation like condensation, randomization and tree structure etc., the limitations of the existing approaches are, they are not able to maintain proper balance between the data utility and privacy and it may have the problem with privacy violations. This paper presents an Additive Rotation Perturbation approach for Privacy Preserving Data Mining (PPDM). In this proposed work, various dataset from UCI Machine Learning Repository was collected and it is protected with a New Additive Rotational Perturbation Technique under Privacy Preserving Data Mining. Experimental result shows that the proposed algorithm’s strength is high for all the datasets and it is estimated using the DoV (Difference of Variance) method.