Privacy-preserving set operations such as set union and set intersection on distributed sets are widely used in data mining in which the. Recently there has been a significant amount of work on privacy-preserving set operations, including: set intersection [14,6,21,9], testing set disjointness . Keywords: multi-party set intersection, privacy-preserving set operation. 1 Introduction. Privacy-Preserving Set Intersection (PPSI) is one of the most interesting.
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Such defective products should be identified as quickly as possible.
However, the databases related to accidents are maintained separately by different organizations. Thus, investigating the causes of accidents is often time-consuming. Let us consider another situation.
Several clinics, denoted as Pi, maintain separate databases, represented as S i. The clinics wish to know the patients they have in common to enable them to share treatment details; however, Pi should not be able to access any information about patients not stored in their own dataset.
In this case, the intersection of the set must not reveal private information. MPSI is executed by multiple parties who jointly compute the intersection of their private datasets.
Privacy preserving set operations, only designated parties can access the intersection.
Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with ZDDs
Previous protocols are impractical, because the bulk of the computation is a function of the number of players. One previous study required the size of the datasets maintained by the different players to be equal [ 1721 ].
Another study [ 11 ] computed only the approximate number of intersections, whereas other researchers [ 18 ] required more than two trusted third-parties.
In this paper, we propose a practical MPSI with the following features: The size of the datasets maintained by each party is independent of those privacy preserving set operations by the other parties.
- Privacy-Preserving Integration of Medical Data
- Privacy-preserving set operations (2005)
The computational complexity for each party is independent of the number of parties. Thus, the number of parties is irrelevant. The remainder of this paper is organized as follows.
We consider security according to the honest-but-curious model [ 13 ]: Note that the term adversary here privacy preserving set operations to insiders, i.
As far as we know, this is the first technique which gives a concrete representation of sets of itemsets and an implementation of set operations for privacy preserving in distributed itemset mining.
Our experiments show that the proposed method provides undistinguishability of dummy data. Furthermore, we compare our method with Secure Multiparty Computation SMCwhich is one of the well-known techniques of secure computation. References 19 Privacy preserving set operations related to the author Supplementary material 0.