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

Abstract

With the growing needs for data sharing and dissemination, privacy-preserving data publishing is becoming an important issue that still requires further investigation. In this paper, we make a step towards private data publication by proposing a solution based on the release of vertical views (fragments) over a relational table that satisfy confidentiality and visibility constraints expressing requirements for information protection and release, respectively. We translate the problem of computing a fragmentation composed of the minimum number of fragments into the problem of computing a maximum weighted clique over a fragmentation graph.The fragmentation graph models fragments, efficiently computed using Ordered Binary Decision Diagrams (OBDDs), that satisfy all the confidentiality constraints and a subset of the visibility constraints defined in the system. We then show an exact and a heuristic algorithm for computing a minimal and a locally minimal fragmentation, respectively. Finally, we provide experimental results comparing the execution time and the fragmentations returned by the exact and heuristic algorithms. The experiments show that the heuristic algorithm has low computation cost and computes a fragmentation close to optimum.

A Neural Data Security Model: Ensure high confidentiality and security in cloud datastorage environment

“…In [3] the author had proposed a data fragmentation method to meet the requirement. The proposed data fragmentation method in this paper improves the data manipulation process, storage optimization, and facilitates data distribution and data security.…”

“…In addition, they attach unencrypted attributes subject to confidentiality constraints in order to allow for efficient selection operations without searchable encryption technique was explained in [3]. Depending on the actual queries, different attribute combinations need to be revealed, resulting in data redundancies.…”

Protecting Privacy in Data Release

“…Function Re_Assign receives as input the set To_Empty of non-empty but underquota groups, and tries to reallocate their tuples to other groups (lines [30][31][32][33][34][35][36][37][38]. Tuples in under-quota groups that cannot be reallocated will be removed from the fragmentation and are inserted into To_Remove (lines [39][40].…”

“…The algorithm then deletes from each fragment both the tuples that have never been assigned to groups and the tuples returned by function Re_Assign (lines [29][30].…”

“…We note however that when multiple original values of X are combined, the approximation in Property 4.3 should be revised to consider the correct number of degrees of freedom, which is equal to the number of values in the domain of X in R rel after the possible merge operation. For instance, with reference to our example, suppose that the values for attribute Age for location L 2 have been combined obtaining the following domain values: Ä 24, [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], [40][41][42][43][44], OE45; 49, 50. The critical value of Pearson's cumulative statistic for L 2 should be computed considering a chi-square distribution with 4 (instead of 9) degrees of freedom.…”