1,446 research outputs found
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
Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data
We consider privacy preserving decision tree induction via ID3 in the case
where the training data is horizontally or vertically distributed. Furthermore,
we consider the same problem in the case where the data is both horizontally
and vertically distributed, a situation we refer to as grid partitioned data.
We give an algorithm for privacy preserving ID3 over horizontally partitioned
data involving more than two parties. For grid partitioned data, we discuss two
different evaluation methods for preserving privacy ID3, namely, first merging
horizontally and developing vertically or first merging vertically and next
developing horizontally. Next to introducing privacy preserving data mining
over grid-partitioned data, the main contribution of this paper is that we
show, by means of a complexity analysis that the former evaluation method is
the more efficient.Comment: 25 page
Privacy-Preserving Decision Tree Classification over Horizontally Partitioned Data
Protection of privacy is one of important problems in data mining. The unwillingness to share their data frequently results in failure of collaborative data mining. This paper studies how to build a decision tree classifier under the following scenario: a database is horizontally partitioned into multiple pieces, with each piece owned by a particular party. All the parties want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces. We build a privacy-preserving system, including a set of secure protocols, that allows the parties to construct such a classifier. We guarantee that the private data are securely protected
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
The k-means clustering is one of the most popular clustering algorithms in
data mining. Recently a lot of research has been concentrated on the algorithm
when the dataset is divided into multiple parties or when the dataset is too
large to be handled by the data owner. In the latter case, usually some servers
are hired to perform the task of clustering. The dataset is divided by the data
owner among the servers who together perform the k-means and return the cluster
labels to the owner. The major challenge in this method is to prevent the
servers from gaining substantial information about the actual data of the
owner. Several algorithms have been designed in the past that provide
cryptographic solutions to perform privacy preserving k-means. We provide a new
method to perform k-means over a large set using multiple servers. Our
technique avoids heavy cryptographic computations and instead we use a simple
randomization technique to preserve the privacy of the data. The k-means
computed has exactly the same efficiency and accuracy as the k-means computed
over the original dataset without any randomization. We argue that our
algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems
Security. Springer, Cham, 201
Decomposable Naive Bayes Classifier for Partitioned Data
Most learning algorithms are designed to work on a single dataset. However, with the growth of networks, data is increasingly distributed over many databases in many different geographical sites. These databases cannot be moved to other network sites due to security, size, privacy, or data ownership consideration. In this paper, we propose two decomposable versions of Naive Bayes Classifier for horizontally and vertically partitioned data. The goal of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging minimum local summaries among the participating sites
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
- …