Privacy Preserving Data Mining Techniques in a Distributed Environment

Lee

et al. 2016

Security Comm Networks

Privacy preservation becomes an important issue in recent big data analysis, and many secure multiparty computations have been proposed for the purpose of privacy preservation in the environment of distributed nodes. As a secure multiparty computations of principal component analysis (PCA), in this paper, we propose S‐PCA, which compute PCA securely among the distributed nodes. PCA is widely used in many applications including time‐series analysis, text mining, and image compression. In general, we compute PCA after concentrating all data in a single server, but this approach discloses data privacy of each node. In contrast, the proposed S‐PCA computes PCA without disclosing the sensitive data of individual nodes. In S‐PCA, the nodes share non‐sensitive mean vectors first and compute covariance matrices and PCA securely using the shared mean vectors. In this paper, we formally prove the correctness and secureness of S‐PCA and apply it to an application of secure similar document detection. Experimental results show that the performance of S‐PCA is slightly worse than that of PCA due to guarantee of secureness, but it significantly improves the performance of secure similar document detection by up to two orders of magnitudes. Copyright © 2016 John Wiley & Sons, Ltd.

Section: Related Workmentioning

confidence: 99%

“…In recent years, there have been many efforts on privacy‐preserving data mining(PPDM), which finds accurate mining results from big data without disclosing their sensitive information . Existing PPDM solutions work on two environments: the centralized and the distributed ones.…”

Section: Introductionmentioning

confidence: 99%

Secure principal component analysis in multiple distributed nodes

Won

Lee

et al. 2016

Security Comm Networks

Research on Cluster Mining Algorithms for Personal Privacy Protection in the Background of Big Data

Huang

2019

J. Phys.: Conf. Ser.

With the rapid development of cloud computing and mobile Internet technology, huge amounts of data are generated on the network at every moment. In the era of big data, most of the data are designed in distributed storage, stored on many independent sites. The typical data mining algorithm can not meet the requirement of privacy data security in the big data background. In the big data background, distributed data mining is faced with the need to solve data security, privacy disclosure and other technical problems. Therefore, this paper applies homomorphic encryption technology to typical K-means clustering mining algorithm, and designs a distributed privacy protection PP-kmeans algorithm based on big data. Experiments show that this algorithm can realize data privacy security protection. And more accurate big data clustering mining effect is achieved.

Efficient Two-Step Protocol and Its Discriminative Feature Selections in Secure Similar Document Detection

Gil

Security and Communication Networks

et al. 2017

Secure similar document detection (SSDD) identifies similar documents of two parties while each party does not disclose its own sensitive documents to another party. In this paper, we propose an efficient 2-step protocol that exploits a feature selection as the lower-dimensional transformation and presents discriminative feature selections to maximize the performance of the protocol. For this, we first analyze that the existing 1-step protocol causes serious computation and communication overhead for high dimensional document vectors. To alleviate the overhead, we next present the feature selection-based 2-step protocol and formally prove its correctness. The proposed 2-step protocol works as follows: (1) in the filtering step, it uses low dimensional vectors obtained by the feature selection to filter out non-similar documents; (2) in the post-processing step, it identifies similar documents only from the non-filtered documents by using the 1-step protocol. As the feature selection, we first consider the simplest one, random projection (RP), and propose its 2-step solution SSDD-RP. We then present two discriminative feature selections and their solutions: SSDD-LF (local frequency) which selects a few dimensions locally frequent in the current querying vector and SSDD-GF (global frequency) which selects ones globally frequent in the set of all document vectors. We finally propose a hybrid one, SSDD-HF (hybrid frequency), that takes advantage of both SSDD-LF and SSDD-GF. We empirically show that the proposed 2-step protocol outperforms the 1-step protocol by three or four orders of magnitude.