Selective data outsourcing for enforcing privacy*

Abstract

Existing approaches for protecting sensitive information outsourced at external "honest-but-curious" servers are typically based on an overlying layer of encryption applied to the whole database, or on the combined use of fragmentation and encryption. In this paper, we put forward a novel paradigm for preserving privacy in data outsourcing, which departs from encryption. The basic idea is to involve the owner in storing a limited portion of the data, while storing the remaining information in the clear at the external server. We analyze the problem of computing a fragmentation that minimizes the owner's workload, which is represented using different metrics and corresponding weight functions, and prove that this minimization problem is NP-hard. We then introduce the definition of locally minimal fragmentation that is used to efficiently compute a fragmentation via a heuristic algorithm. The algorithm translates the problem of finding a locally minimal fragmentation in terms of a hypergraph 2-coloring problem. Finally, we illustrate the execution of queries on fragments and provide experimental results comparing the fragmentations returned by our heuristics with respect to optimal fragmentations. The experiments show that the heuristics guarantees a low computation cost and is able to compute a fragmentation close to optimum.

Loose associations to increase utility in data publishing1

“…To this purpose, the algorithm orders the tuples in s according to the attributes in A ∩ F i (line 5). It then partitions the ordered tuples in sets of k i contiguous tuples each, and assigns to each partition a different group identifier g i current (lines [6][7][8][9][10][11][12][13]. In this way, contiguous tuples in the ordered relation are ideally assigned to the same group (or to contiguous groups).…”

“…Several research efforts have addressed the problem of protecting privacy in data publishing, proposing approaches based on either sanitizing (e.g., [10,15,16,[19][20][21]23,30]) or fragmenting data (e.g., [1,2,[7][8][9]11,29]) before their release. Our approach bears some similarity with k-anonymity [24] for the notion of grouping and more similarity with -diversity [21] for the consideration of the different values that are involved in the sensitive associations.…”

Privacy in Pervasive Systems: Social and Legal Aspects and Technical Solutions

“…The adoption of encryption to satisfy confidentiality can be (partially) avoided storing the collected data through a set of privacy-preserving views, which are defined in such a way to satisfy confidentiality constraints [1,12,13,14,15]. To this aim, sensitive associations among attributes are broken (fragmented) by storing the attributes composing each of them in different views.…”

Enforcing Privacy in Cloud Databases

“…What is sensitive is their association with other data. Privacy is thus guaranteed by concealing such associations with respect to a predefined set of security constraints that express restrictions on one or more attributes in a table [20,37]. For instance, given a P atient table, constraint C = {N ame, Illness} indicates that associations between patient names and illnesses should not be disclosed.…”