Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution

Tunable Security for Deployable Data Outsourcing

Security mechanisms like encryption negatively affect other software quality characteristics like efficiency. To cope with such trade-offs, it is preferable to build approaches that allow to tune the trade-offs after the implementation and design phase. This book introduces a methodology that can be used to build such tunable approaches. The book shows how the proposed methodology can be applied in the domains of database outsourcing, identity management, and credential management