|
|
|
| e-Pub |
Previous | 
Home
Section: New Results
Query Processing
Top-k Query Processing Over Encrypted Data in the Cloud
Participants : Sakina Mahboubi, Reza Akbarinia, Patrick Valduriez.
Cloud computing provides users and companies with powerful capabilities to store and process their data in third-party data centers. However, the privacy of the outsourced data is not guaranteed by the cloud providers. One solution for protecting the user data against security attacks is to encrypt the data before being sent to the cloud servers. Then, the main problem is to evaluate user queries over the encrypted data.
In [12], we propose a system, called SD-TOPK (Secure Distributed TOPK), that encrypts and stores user data in a cloud across a set of nodes, and is able to evaluate top-k queries over the encrypted data. SD-TOPK comes with a novel top-k query processing algorithm that finds a set of encrypted data that is proven to contain the top-k data items. This is done without having to decrypt the data in the nodes where they are stored. In addition, we propose a powerful filtering algorithm that removes the false positives as much as possible without data decryption. We implemented and evaluated the performance of our system over synthetic and real databases. The results show excellent performance for SD-TOPK compared to TA-based approaches.
Parallel Query Rewriting in Key-Value Stores under Single-Key Constraints
Participant : Reza Akbarinia.
Semantic constraints bring important knowledge about the structure and the domain of data. They allow users to better exploit their data thanks to the possibility of formulating high-level queries, which use a vocabulary richer than that of the single sources. However, the constraint-based rewriting of a query may lead to a huge set of new queries, which has a consequent impact on the query answering time.
In [37], we propose a novel technique for parallelizing both the generation and the evaluation of the rewriting set of a query serving as the basis for distributed query evaluation under constraints. Our solution relies on a schema for encoding the possible rewritings of a query on an integer interval. This allows us to generate equi-size partitions of rewritings, and thus to balance the load of the parallel working units that are in charge of generating and evaluating the queries. The experimental evaluation of our technique shows a significant reduction of query rewriting and execution time by means of parallelization.