An Index Structure for Private Data Outsourcing

Abstract

Abstract. Data outsourcing provides companies a cost effective method for their data to be stored, managed, and maintained by a third-party. Data outsourcing offers many economical benefits, but also introduces several privacy concerns. Many solutions have been proposed for maintaining privacy while outsourcing data in the data as plain-text model. We propose a method that can maintain a similar level of privacy while improving upon the query performance of previous solutions. The motivating principle behind our solution is that if the data owner possesses a small amount of secure local storage, it can be used as a pseudo-index table to improve query performance for selection queries involving conjunctions. We offer a heuristic approach for calculating the required storage resources and provide experimental analysis of the scheme.

An OBDD approach to enforce confidentiality and visibility constraints in data publishing*

“…In fact, a clique is a solution of the fragmentation problem only when, for each visibility constraint v in V, there is a node in the clique such that v is associated with such a node. For this reason, the algorithm removes n i from G (lines [26][27][28][29].…”

“…To this purpose, the algorithm first assigns value 0 to don't care variables in the truth assignments represented by the nodes in C (line 20). Then, it iteratively extracts a node n i from C, assigns n i .I to I i and, for each n j in C, it checks if I i can be composed with one-path I j , with I j = n j .I without violating confidentiality constraints (lines [21][22][23][24][25][26][27][28]. If this is the case, I i is set to I i ∨I j , and n j is removed from C. When the algorithm has checked if I i can be combined with all the one-paths represented by nodes in C, it inserts I i in I sol (line 29).…”

“…The proposals based on fragmentation can be classified as solutions that: 1) combine fragmentation and encryption [1,9,11]; and 2) depart from encryption and satisfy confidentiality constraints by splitting the data over two fragments, one of which is stored at the data holder site [8,10,28,29]. Among the first class of solutions, the approach introduced in [1] relies on the presence of two non-communicating servers.…”

Secure Scalar Product for Big-Data in MapReduce

“…Nonetheless, although data outsourcing becomes more and more popular, the server and cloud environment, often managed by a third party, are not fully trustful for users, and security and data privacy become primary concerns for users [24], [29], [31], [36]. To guarantee the confidentiality and privacy of user's data, data is often encrypted first before outsourcing, so that only authorized parties, for example the data owner, have the right to access the data and know the plaintext of it.…”

On information leakage by indexes over data fragments

“…These conditions would then need to be evaluated by the client, who can access the whole dataset, with potentially high cost in terms of communication and clientside computation. To avoid such a limitation and effectively exploit fragmentation, it is important to couple fragments with indexes on the encrypted portion of the data enabling some server-side evaluation of conditions on attributes not appearing in the clear [6]. In this paper, we discuss the combined use of fragmentation and indexes and show how it may cause leakage of confidential information, otherwise protected by encryption or fragmentation.…”