Real and substantial connections : Enforcing Canadian privacy laws against American social networking companies

Any organisation that captures personal data in Canada for processing is deemed tohave a ‘real and substantial connection’ to Canada and thus fall within thejurisdiction of the Personal Information Protection and Electronic Documents Act(PIPEDA) and of the Office of the Privacy Commissioner of Canada (OPC). Whathas been the experience of enforcing Canadian privacy protection law on US-basedsocial networking services? We analyse some of the high-profile enforcement actionsby the Privacy Commissioner. We also test compliance through an analysis of theprivacy policies of the top 23 SNSs operating in Canada and through the use of accessto personal information requests. Our analysis suggests that non-compliance iswidespread, and is explained by the countervailing conceptions of jurisdictioninherent in corporate policy and technical system design.

PPFSCADA: Privacy preserving framework for SCADA data publishing

Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as electricity, gas, water, waste, railway, and traffic. Recent attacks on SCADA systems highlight the need for stronger SCADA security. Thus, sharing SCADA traffic data has become a vital requirement in SCADA systems to analyze security risks and develop appropriate security solutions. However, inappropriate sharing and usage of SCADA data could threaten the privacy of companies and prevent sharing of data. In this paper, we present a privacy preserving strategy-based permutation technique called PPFSCADA framework, in which data privacy, statistical properties and data mining utilities can be controlled at the same time. In particular, our proposed approach involves: (i) vertically partitioning the original data set to improve the performance of perturbation; (ii) developing a framework to deal with various types of network traffic data including numerical, categorical and hierarchical attributes; (iii) grouping the portioned sets into a number of clusters based on the proposed framework; and (iv) the perturbation process is accomplished by the alteration of the original attribute value by a new value (clusters centroid). The effectiveness of the proposed PPFSCADA framework is shown through several experiments on simulated SCADA, intrusion detection and network traffic data sets. Through experimental analysis, we show that PPFSCADA effectively deals with multivariate traffic attributes, producing compatible results as the original data, and also substantially improving the performance of the five supervised approaches and provides high level of privacy protection. © 2014 Published by Elsevier B.V. All rights reserved.

Privacy preserving in location data release: A differential privacy approach

Communication devices with GPS chips allow people to generate large volumes of location data. However, location datasets have been confronted with serious privacy concerns. Recently, several privacy techniques have been proposed but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location datasets in a strict privacy notion, differential privacy. This algorithm includes three privacy-preserving operations: Private Location Clustering shrinks the randomized domain and Cluster Weight Perturbation hides the weights of locations, while Private Location Selection hides the exact locations of a user. Theoretical analysis on utility confirms an improved trade-off between the privacy and utility of released location data. The experimental results further suggest this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

Chameleon hashing for secure and privacy-preserving vehicular communications

Many services and applications in vehicular ad-hoc networks (VANETs) require preserving and secure data communications. To improve driving safety and comfort, the traffic-related status information will be broadcasted regularly and shared among drivers. Without the security and privacy guarantees, attackers could track their interested vehicles by collecting and analyzing their traffic messages. Hence, anonymous message authentication is an essential requirement of VANETs. On the other hand, when a vehicle is involved in a dispute event of warning message, the certificate authority should be able to recover the real identity of this vehicle. To deal with this issue, we propose a new privacy-preserving authentication protocol with authority traceability using elliptic curve based chameleon hashing. Compared with existing schemes, our approach possesses the following features: 1) mutual and anonymous authentication for both vehicle-to-vehicle and vehicle-to-roadside communications, 2) vehicle unlinkability, 3) authority tracking capability, and 4) high computational efficiency. We also demonstrate the merits of our proposed scheme through security analysis and extensive performance evaluation.

Location privacy preserving for semantic-aware applications

With the increase use of location-based services, location privacy has recently raised serious concerns. To protect a user from being identified, a cloaked spatial region that contains other k-1 nearest neighbors of the user is used to replace the accurate position. In this paper, we consider location-aware applications that services are different among regions. To search nearest neighbors, we define a novel distance measurement that combines the semantic distance and the Euclidean distance to address the privacy preserving issue in the above-mentioned applications. We also propose an algorithm kNNH to implement our proposed method. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users’ privacy.

A survey on differential privacy and applications

Privacy preserving in data release and mining is a hot topic in the information security field currently. As a new privacy notion, differential privacy (DP) has grown in popularity recently due to its rigid and provable privacy guarantee. After analyzing the advantage of differential privacy model relative to the traditional ones, this paper surveys the theory of differential privacy and its application on two aspects, privacy preserving data release (PPDR) and privacy preserving data mining (PPDM). In PPDR, we introduce the DP-based data release methodologies in interactive/non-interactive settings and compare them in terms of accuracy and sample complexity. In PPDM, we mainly summarize the implementation of DP in various data mining algorithms with interface-based/fully access-based modes as well as evaluating the performance of the algorithms. We finally review other applications of DP in various fields and discuss the future research directions.

An effective privacy preserving algorithm for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has been the focus of significant attention in recent years, however, its privacy-related issues, especially for the neighborhood-based CF methods, cannot be overlooked. The aim of this study is to address these privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. This algorithm includes two privacy preserving operations: Private Neighbor Selection and Perturbation. Using the item-based method as an example, Private Neighbor Selection is constructed on the basis of the notion of differential privacy, meaning that neighbors are privately selected for the target item according to its similarities with others. Recommendation-Aware Sensitivity and a re-designed differential privacy mechanism are introduced in this operation to enhance the performance of recommendations. A Perturbation operation then hides the true ratings of selected neighbors by adding Laplace noise. The PNCF algorithm reduces the magnitude of the noise introduced from the traditional differential privacy mechanism. Moreover, a theoretical analysis is provided to show that the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations. The results from experiments on two real datasets show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss. © 2013 Published by Elsevier B.V. All rights reserved.

Aggregating privatized medical data for secure querying applications

 This thesis analyses and examines the challenges of aggregation of sensitive data and data querying on aggregated data at cloud server. This thesis also delineates applications of aggregation of sensitive medical data in several application scenarios, and tests privatization techniques to assist in improving the strength of privacy and utility.