Partitioned Data Security on Outsourced Sensitive and Non-Sensitive Data

Abstract

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We propose a new secure approach, entitled query binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by the joint processing of non-sensitive data (in cleartext) and sensitive data (in encrypted form). QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. Interestingly, in addition to improve performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.

ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services

“…For example, Mouratidis and Yiu [29] proposed a hardware-aided PIR-based solution to PPSP that relies on a tamper-resistant secure co-processor installed at the server-side. However, such schemes require decrypting the data in a secure area at the cloud and perform the computation on decrypted data; thus, they do not protect data access patterns from the server [30].…”

Panda

“…A preliminary version of this article was accepted and presented in IEEE ICDE [53]. The conference version includes the following additional concept, which is not provided in this version, due to space restriction: an analytical model to show when QB works better compared to a pure cryptographic technique (Section V A of Reference [53]).…”

“…A preliminary version of this article was accepted and presented in IEEE ICDE [53]. The conference version includes the following additional concept, which is not provided in this version, due to space restriction: an analytical model to show when QB works better compared to a pure cryptographic technique (Section V A of Reference [53]). semantic data modeling [38], (ii) user-defined relationships between sensitive and non-sensitive data [67], (iii) constraintsbased mechanisms, (iv) sensitive patterns hiding using sanitization matrix [44], and (v) common knowledge-based association rules [45].…”

“…The EId attribute values corresponding to sensitive tuples include E101, E259, E152, E159 and corresponding to non-sensitive tuples are E199, E259, E254, E152 for which a = is 1:1. We discuss QB under the above assumption, but these assumptions are relaxed in the conference version of this article (please see Sections IV.A and IV.B in Reference [53]). Before describing QB, we first define the concept of approximately square factors of a number.…”

Scaling Cryptographic Techniques by Exploiting Data Sensitivity at a Public Cloud

“…Now, the DB owner arranges 16 values in a 4 × 4 matrix, as follows: , 13,1,12,9) on the sensitive data using the cryptographic mechanism integrated into QB. While the adversary learns that the query corresponds to one of the four values in NSB 1 , since query values in SB 3 are encrypted, the adversary does not learn 1 These assumptions are made primarily for ease of the exposition and relaxed in [16].…”