The impact of privacy requirements in the development of modern applications is increasing very quickly. Many commercial and legal regulations are driving the need to develop reliable solutions for protecting sensitive information whenever it is stored, processed, or communicated to external parties. To this purpose, encryption techniques are currently used in many scenarios where data protection is required since they provide a layer of protection against the disclosure of personal information, which safeguards companies from the costs that may arise from exposing their data to privacy breaches. However, dealing with encrypted data may make query processing more expensive.In this article, we address these issues by proposing a solution to enforce the privacy of data collections that combines data fragmentation with encryption. We model privacy requirements as confidentiality constraints expressing the sensitivity of attributes and their associations. We then This article extends the previous work by the authors appeared under the title Fragmentation and Encryption to Enforce Privacy in Data Storage in 22:2 • V. Ciriani et al.use encryption as an underlying (conveniently available) measure for making data unintelligible while exploiting fragmentation as a way to break sensitive associations among attributes. We formalize the problem of minimizing the impact of fragmentation in terms of number of fragments and their affinity and present two heuristic algorithms for solving such problems. We also discuss experimental results, comparing the solutions returned by our heuristics with respect to optimal solutions, which show that the heuristics, while guaranteeing a polynomial-time computation cost are able to retrieve solutions close to optimum.

No abstract

We put forward a novel paradigm for preserving privacy in data outsourcing which departs from encryption. The basic idea behind our proposal is to involve the owner in storing a limited portion of the data, and maintaining all data (either at the owner or at external servers) in the clear. We assume a relational context, where the data to be outsourced is contained in a relational table . We then analyze how the relational table can be fragmented, minimizing the load for the data owner. We propose several metrics and present a general framework capturing all of them, with a corresponding algorithm finding a heuristic solution to a family of NP-hard problems.

No abstract

No abstract