3,777 research outputs found
Secret charing vs. encryption-based techniques for privacy preserving data mining
Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing
With the onset of the Information Era and the rapid growth of information
technology, ample space for processing and extracting data has opened up.
However, privacy concerns may stifle expansion throughout this area. The
challenge of reliable mining techniques when transactions disperse across
sources is addressed in this study. This work looks at the prospect of creating
a new set of three algorithms that can obtain maximum privacy, data utility,
and time savings while doing so. This paper proposes a unique double encryption
and Transaction Splitter approach to alter the database to optimize the data
utility and confidentiality tradeoff in the preparation phase. This paper
presents a customized apriori approach for the mining process, which does not
examine the entire database to estimate the support for each attribute.
Existing distributed data solutions have a high encryption complexity and an
insufficient specification of many participants' properties. Proposed solutions
provide increased privacy protection against a variety of attack models.
Furthermore, in terms of communication cycles and processing complexity, it is
much simpler and quicker. Proposed work tests on top of a realworld transaction
database demonstrate that the aim of the proposed method is realistic
Privacy Preserving Access of Outsourced Data in Heterogeneous Databases
- Privacy is main concern in the world, among present technological phase. Information security has become a dangerous issue since the information sharing has a common need. Recently, privacy issues have been increased enormously when internet is flourishing with forums, social media, blogs and e-commerce, etc. Hence research area is retaining privacy in data mining. The sensitive data of the data owners should not be known to the third parties and other data owners. To make it efficient, the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency. we address the major issues of privacy preservation in information mining. In particular, we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data2019;s are available in the heterogeneous databases. Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy. Symmetric homomorphic encryption scheme, which is significantly more efficient than the asymmetric schemes. Our proposed work helps the valid user can extract with key issue in partition data in automated approach and the data2019;s are partitioned horizontally
Modeling the Product Space as a Network
In the market basket setting, we are given a series of transactions each
composed of one or more items and the goal is to find relationships
between items, usually sets of items that tend to occur in the same
transaction. Association rules, a popular approach for mining such data,
are limited in the ability to express complex interactions between
items. Our work defines some of these limitations and addresses them by
modeling the set of transactions as a network. We develop both a general
methodology for analyzing networks of products, and a privacy-preserving
protocol such that product network information can be securely shared
among stores. In general, our network based view of transactional data
is able to infer relationships that are more expressive and expansive
than those produced by a typical association rules analysis
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