A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters

Abstract : Several secure distributed data mining methods have been proposed in the literature that are based on privacy preserving set operation mechanisms. However, they are limited in the scalability of both the size and the number of data owners (sources). Most of these techniques are primarily designed to work with two data owners and extensions to handle multiple owners are either expensive or infeasible. In addition, for large datasets, they incur substantial communication/computation overhead due to the use of cryptographic techniques. In this paper, we propose a scalable privacy-preserving protocol that approximates global itemset support, without employing any cryptographic mechanism. We also present some emperical results to demonstrate the effectiveness of our approach.
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Communication dans un congrès
David Hutchison; Takeo Kanade; Bernhard Steffen; Demetri Terzopoulos; Doug Tygar; Gerhard Weikum; Vijay Atluri; Günther Pernul; Josef Kittler; Jon M. Kleinberg; Alfred Kobsa; Friedemann Mattern; John C. Mitchell; Moni Naor; Oscar Nierstrasz; C. Pandu Rangan. 28th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2014, Vienna, Austria. Springer, Lecture Notes in Computer Science, LNCS-8566, pp.382-389, 2014, Data and Applications Security and Privacy XXVIII. 〈10.1007/978-3-662-43936-4_26〉
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Vikas Ashok, Ravi Mukkamala. A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters. David Hutchison; Takeo Kanade; Bernhard Steffen; Demetri Terzopoulos; Doug Tygar; Gerhard Weikum; Vijay Atluri; Günther Pernul; Josef Kittler; Jon M. Kleinberg; Alfred Kobsa; Friedemann Mattern; John C. Mitchell; Moni Naor; Oscar Nierstrasz; C. Pandu Rangan. 28th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2014, Vienna, Austria. Springer, Lecture Notes in Computer Science, LNCS-8566, pp.382-389, 2014, Data and Applications Security and Privacy XXVIII. 〈10.1007/978-3-662-43936-4_26〉. 〈hal-01284874〉

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