Design of a Privacy-Preserving Data Mining System Based on Differential Privacy Using Additive-Homomorphic Proxy Re-Encryption Protocol Against Insider Attacks
Date Issued
2016
Date
2016
Author(s)
Wang, Peter Shaojui
Abstract
In this thesis, we consider a new insider threat for the privacy preserving work of distributed kernel-based data mining (DKBDM), such as distributed Support Vector Machine (SVM). Among several known data breaching problems, those associated with insider attacks have been rising significantly, making this one of the fastest growing types of security breaches. Once considered a negligible concern, insider attacks have risen to be one of the top three central data violations. Insider-related research involving the distribution of kernel-based data mining is limited, resulting in substantial vulnerabilities in designing protection against “collaborative organizations.” Prior works often fall short by addressing a multifactorial model that is more limited in scope and implementation than addressing “insiders within an organization” colluding with outsiders. A faulty system allows collusion to go unnoticed when an insider shares data with an outsider, who can then recover the original data from message transmissions (intermediary kernel values) among organizations. This attack requires only accessibility to a few data entries within the organizations rather than requiring the encrypted administrative privileges typically found in the distribution of data mining scenarios. To the best of our knowledge, we are the first to explore this new insider threat in DKBDM. We also analytically demonstrate the minimum amount of insider data necessary to launch the insider attack. For countering the described attack, we then present two privacy-preserving methods to defend against the attack. For the first method, we reduce the number of insiders or expand the data dimensions to prevent the satisfaction of the privacy breach rule. For the second method, as differential privacy is one of the most theoretically sound and widespread privacy concepts, we will prove differential private method effective against the serious insider attack. Besides, Homomorphic Encryption method, which allows calculations on encrypted information to be performed without first decrypting the information, has been successfully used to solve the privacy issue of DKBDM in the past. However, this method is too time-consuming. Thus, we propose a Differentially-Private model based on Additive Homomorphic Proxy Re-Encryption for SVM (DAHOPE-SVM), which can drastically reduce the use of Homomorphic Encryption with the help of Proxy Re-Encryption and thus reduce the time required to perform. Our proposed method has been the quickest method of applying Homomorphic Encryption in DKBDM until now; at the same time, our method maintains a high standard of privacy protection by including a proven differential privacy component.
Subjects
Data Mining
Privacy Protection
Insider Attack
Kernel Function
Differential Privacy
Type
thesis