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Secure multiparty computation allows multiple parties to participate in a computation. SMC (secure multiparty computation) assumes n parties where n>1. All the parties jointly compute a function. Privacy preserving data mining has become an emerging field in the secure multiparty computation. Privacy preserving data mining preserves the privacy of individual's data. Privacy preserving data mining...
Secure multi-party computation (SMC) deals with the problem of secure computation among participants who are not trusted by others. Privacy preserving computational geometry is a special area in SMC and has been applied to various of areas such as military, commerce and governments et al. In this paper, we will propose an efficient secure protocol, which is based on numerical computation, for the...
In this paper, we perform an empirical analysis of email traffic logs obtained from a large university to better understand the development of social networks. We analyzed data containing records of emails sent over a period of 10 months - the largest dataset we are aware of. We study the long term evolution of social networks on real world data. The initial analysis of data is followed by an exploration...
A protocol is secure if the parties who want to compute their inputs hands it to the trusted parties. Trusted parties in turn compute the inputs using the function f and give the result to the respective parties after computation in such a way that no party can identify other's party data. During computation of inputs, we had considered the factor, what if trusted third parties are malicious? Considering...
Private-preserving shared dot product protocol is an important protocol of many secure multi-party computation problems. It has been the main building block of various data mining algorithms with privacy concerns, and providing fundamental security guarantee for many privacy-preserving data mining algorithms. In this paper, we construct a privacy-preserving two-party shared dot product protocol based...
Privacy-preserving data mining aims at securely extracting knowledge from two or more parties' private data. Secure multi-party computation is the paramount approach to it. In this paper, we study privacy-preserving add and multiply exchanging technology and present three new different approaches to privacy-preserving add to multiply protocol. After that, we analyze and compare the three different...
The following topics are dealt with: biometrics; forensic identification; identity management; security; privacy; socio-legal aspect; and identity system evaluation.
Information privacy typically concerns the confidentiality of personal identifiable information (PII) and protected health information (PHI) such as electronic medical records. Thus, the information access control mechanism for e-health services must be embedded with privacy-enhancing technologies. Role-based access control (RBAC) model has been widely investigated and applied to various applications...
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