Fuzzy Range Query in XML

This writing project presents a new approach to implement a fuzzy range query solution for retrieving Extensible Markup Language (XML) data. Ever since XML was introduced, it has become a web standard to describe data on the Internet. The need for performing range query against XML data is growing day by day. Many search service providers are eager to improve their solutions on range query against XML data. The project studies and analyzes the limitations on the current range query solutions. The project also proposes a new solution using fuzzy semantic analysis to quantify XML data so that it can be represented within a range. This is accomplished by applying fuzzy logic algorithm to classify and aggregate XML data based on the semantic closeness. An intuitive web interface is also introduced to aid the user to input fuzzy search criteria. Instead of specifying crisp values in the current solutions, the user can simply drag and drop to indicate fuzzy values. Therefore, it’s more user-friendly and desirable for fuzzy range query

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

Efficient range query processing in peer-to-peer systems

2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Improved Reconstruction Attacks on Encrypted Data Using Range Query Leakage

We analyse the security of database encryption schemes supporting range queries against persistent adversaries. The bulk of our work applies to a generic setting, where the adversary's view is limited to the set of records matched by each query (known as access pattern leakage). We also consider a more specific setting where certain rank information is also leaked. The latter is inherent to multiple recent encryption schemes supporting range queries, including Kerschbaum's FH-OPE scheme (CCS 2015), Lewi and Wu's order-revealing encryption scheme (CCS 2016), and the recently proposed Arx scheme of Poddar et al. (IACR eprint 2016/568, 2016/591). We provide three attacks. First, we consider full reconstruction, which aims to recover the value of every record, fully negating encryption. We show that for dense datasets, full reconstruction is possible within an expected number of queries NlogN+O(N)Nlog⁡N+O(N), where NN is the number of distinct plaintext values. This directly improves on a O(N2logN)O(N2log⁡N) bound in the same setting by Kellaris et al. (CCS 2016). We also provide very efficient, data-optimal algorithms that succeed with the minimum possible number of queries (in a strong, information theoretical sense), and prove a matching data lower bound for the number of queries required. Second, we present an approximate reconstruction attack recovering all plaintext values in a dense dataset within a constant ratio of error (such as a 5% error), requiring the access pattern leakage of only O(N)O(N) queries. We also prove a matching lower bound. Third, we devise an attack in the common setting where the adversary has access to an auxiliary distribution for the target dataset. This third attack proves highly effective on age data from real-world medical data sets. In our experiments, observing only 25 queries was sufficient to reconstruct a majority of records to within 5 years. In combination, our attacks show that current approaches to enabling range queries offer little security when the threat model goes beyond snapshot attacks to include a persistent server-side adversary