Secure multidimensional range queries over outsourced data

Abstract

In this paper, we study the problem of supporting multidimensional range queries on encrypted data. The problem is motivated by secure data outsourcing applications where a client may store his/her data on a remote server in encrypted form and want to execute queries using server's computational capabilities. The solution approach is to compute a secure indexing tag of the data by applying bucketization (a generic form of data partitioning) which prevents the server from learning exact values but still allows it to check if a record satisfies the query predicate. Queries are evaluated in an approximate manner where the returned set of records may contain some false positives. These records then need to be weeded out by the client which comprises the computational overhead of our scheme. We develop a bucketization procedure for answering multidimensional range queries on multidimensional data. For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client's computational overhead and disclosure risk respectively. Given a multidimensional dataset, its bucketization is posed as an optimization problem where the goal is to minimize the risk of disclosure while keeping query cost (client's computational overhead) below a certain user-specified threshold value. We provide a tunable data bucketization algorithm that allows the data owner to control the trade-off between disclosure risk and cost. We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data.

Enforcing Privacy in Cloud Databases

“…They also introduce a re-bucketization technique, in which the user can fine-tune bucketization to achieve a desired level of privacy. Moreover, they propose a new method for answering range queries on multidimensional data [38]. Range queries over multiple attributes, e.g., age < 20 and salary > 25k, are allowed, while minimizing the cost of multidimensional bucketization.…”

Protecting Privacy in the Cloud: Current Practices, Future Directions

“…• Efficient Query-specific Encryption: This kind of encryption techniques [6,9] are designed to allow the cloud to efficiently execute specific classes of queries such as keyword search queries, range queries, and aggregation queries without decrypting the data. For example, for answering (multi-dimensional) range queries, a combination of encryption and data partitioning techniques can be used to organize data elements into 'buckets' that can be stored safely in a public cloud [9].…”

“…For example, for answering (multi-dimensional) range queries, a combination of encryption and data partitioning techniques can be used to organize data elements into 'buckets' that can be stored safely in a public cloud [9]. The solution requires clients (i.e.…”

“…Encryption [6,7,8,9] Homomorphic Encryption [7] All types of queries Strong -Impractical for real-life applications due to prohibitive computation cost.…”

“…Query-specific Encryption [6,9] Keyword search queries [6] Strong -Substantial computation overhead for data owners, -Not all queries are supported.…”