In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed pr...

Abstract Protecting users’ privacy in digital systems becomes more complex and challenging over time, as the amount of stored exchanged data grows steadily become increasingly involved connected. Two techniques that try to approach this issue are privacy-preserving protocols secure multi-party computation (MPC) private information retrieval (PIR), which aim enable practical while simultaneously...

Abstract In this paper, we address the problem of privacy-preserving distributed learning and evaluation machine-learning models by analyzing it in widespread MapReduce abstraction that extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), first system covers complete ML workflow enabling execution a cooperative gradient-descent obtained model pr...

Resource Constrained Devices (RCD) in general construct the pervasive computing environment which are equipped with too limited resources to deploy privacy preserving data mining applications. This paper proposes a communication efficient and perturbation based privacy preserving association rule mining (ARM) algorithm for this ubiquitous computing environment. Existing cryptography based priva...

In view of the issue of internal data in the construction of recommendation system may be leaked by external person. A privacy-preserving recommendation system based on MASK is designed to address it. The system consists of two parts: privacy-preserving subsystem and recommendation subsystem. First, disrupt original data to form new privacy-preserving data according to a certain probability. Se...

In this paper, we introduce the first protocols for multi-party, privacy-preserving, fair reconciliation of ordered sets. Our contributions are twofold. First, we show that it is possible to extend the round-based construction for fair, two-party privacypreserving reconciliation of ordered sets to multiple parties using a multi-party privacy-preserving set intersection protocol. Second, we prop...

In response to the growing interest in genome-wide association study (GWAS) data privacy, the Integrating Data for Analysis, Anonymization and SHaring (iDASH) center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healt...

There is a tremendous increase in the research of data mining. Data mining is the process of extraction of data from large database. Knowledge Discovery in database (KDD) is another name of data mining. Privacy protection has become a necessary requirement in many data mining applications due to emerging privacy legislation and regulations. One of the most important topics in research community...