A privacy-preserved indexing schema in DaaS model for range queries

In a database-as-a-service(DaaS)model,a data owner stores data in a database server of a service provider,and the DaaS adopts the encryption for data privacy and indexing for data query.However,an attacker can obtain original data’s statistical information and distribution via the indexing distribution from the database of the service provider.In this work,a novel indexing schema is proposed to satisfy privacy-preserved data management requirements,in which an attacker cannot obtain data source distribution or statistic information from the index.The approach includes 2 parts:the Hash-based indexing for encrypted data and correctness verification for range queries.The evaluation results demonstrate that the approach can hide statistical information of encrypted data distribution while can also obtain correct answers for range queries.Meanwhile,the approach can achieve nearly 10 times and 35 times improvement on encrypted data publishing and indexing respectively,compared with the start-of-the-art method order-preserving Hash-based function(OPHF).

CLEAN:Frequent Pattern-Based Trajectory Compression and Computation on Road Networks

The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

Search geometric ranges efficiently as keywords over encrypted spatial data

With the increasing popularity of location-based services(LBS),data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers.Geometric range queries are one of the fundamental search functions in LBS,which are to find points inside geometric areas(e.g.,circles or polygons).To ensure data confidentiality,the service users tend to encrypt the data before outsourcing it.However,regarding encrypted data,only a few consider geometric range queries,where the rationale is the high-dimension calculations make these queries particularly harder.In this paper,we propose a novel scheme for geometric range queries,that can provide the privacy of data stored at a cloud server and queries.Our scheme supports querying encrypted spatial data with irregular-shaped areas,achieves fast searches and enables dynamic updates.Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4×compared to state-of-the-art schemes,without carrying any potentially visible leakage in the structure.

Differential Privacy via a Truncated and Normalized Laplace Mechanism

When querying databases containing sensitive information,the privacy of individuals stored in the database has to be guaranteed.Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses.However,most such mechanisms do not take into consideration the valid range of the query being posed.Thus,noisy responses that fall outside of this range may potentially be produced.To rectify this and therefore improve the utility of the mechanism,the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized.However,such a data-dependent operation of normalization leaks additional information about the true query response,thereby violating the differential privacy guarantee.Here,we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution.We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations.We provide derivations of the optimal scaling parameter(i.e.,the minimal value that preserves differential privacy)for each class or provide an approximation thereof.As a result of this work,one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner.To demonstrate the benefits of our proposed method of normalization,we present an experimental comparison against other range-adherent mechanisms.We show that our proposed approach is able to provide improved utility over the alternative mechanisms.