Solution For Query Processing Research Articles

Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results while satisfying the privacy guarantees. Previous work, notably Li et al. [2010], has suggested that, with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose a low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., ϵ-) and approximate (i.e., (ϵ, δ)-) differential privacy definitions. We derive the utility guarantees of LRM and provide guidance on how to set the privacy parameters, given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.

SHP: A Hierarchical Protocol to Improve Performance of Peer-to-Peer Systems

Extensive application of Peer-to-Peer systems demands an effective solution for effic ient query processing, handling of churn rate,load balancing and maintenance of healthy arrangement of nodes for the improved response of the system. Several key based systems offeran efficient solution for query processing but suffer from transient node population. We present a structured hierarchical Peer -to-Peer system to reduce overhead caused by churn rate. The simulation result shows better performance than existing structured systems while executes point query and range query.

Low-rank mechanism

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably the matrix mechanism [16], has suggested that processing a batch of correlated queries as a whole can potentially achieve considerable accuracy gains, compared to answering them individually. However, as we point out in this paper, the matrix mechanism is mainly of theoretical interest; in particular, several inherent problems in its design limit its accuracy in practice, which almost never exceeds that of naïve methods. In fact, we are not aware of any existing solution that can effectively optimize a query batch under differential privacy. Motivated by this, we propose the Low-Rank Mechanism (LRM), the first practical differentially private technique for answering batch queries with high accuracy, based on a low rank approximation of the workload matrix. We prove that the accuracy provided by LRM is close to the theoretical lower bound for any mechanism to answer a batch of queries under differential privacy. Extensive experiments using real data demonstrate that LRM consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.

Parallel OLAP query processing in database clusters with data replication

We consider the problem of improving the performance of OLAP applications in a database cluster (DBC), which is a low cost and effective parallel solution for query processing. Current DBC solutions for OLAP query processing provide for intra-query parallelism only, at the cost of full replication of the database. In this paper, we propose more efficient distributed database design alternatives which combine physical/virtual partitioning with partial replication. We also propose a new load balancing strategy that takes advantage of an adaptive virtual partitioning to redistribute the load to the replicas. Our experimental validation is based on the implementation of our solution on the SmaQSS DBC middleware prototype. Our experimental results using the TPC-H benchmark and a 32-node cluster show very good speedup.

Privacy Protected Spatial Query Processing for Advanced Location Based Services

Due to the popularity of mobile devices (e.g., cell phones, PDAs, etc.), location-based services have become more and more prevalent in recent years. However, users have to reveal their location information to access location-based services with existing service infrastructures. It is possible that adversaries could collect the location information, which in turn invades user’s privacy. There are existing solutions for query processing on spatial networks and mobile user privacy protection in Euclidean space. However there is no solution for solving queries on spatial networks with privacy protection. Therefore, we aim to provide network distance spatial query solutions which can preserve user privacy by utilizing K-anonymity mechanisms. In this paper, we propose an effective location cloaking mechanism based on spatial networks and two novel query algorithms, PSNN and PSRQ, for answering nearest neighbor queries and range queries on spatial networks without revealing private information of the query initiator. We demonstrate the appeal of our technique using extensive simulation results.