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A comparative study of microaggregation methods.

Josep Maria Mateo Sanz, Josep Domingo Ferrer (1998)

Qüestiió

Microaggregation is a statistical disclosure control technique for microdata. Raw microdata (i.e. individual records) are grouped into small aggregates prior to publication. Each aggregate should contain at least k records to prevent disclosure of individual information. Fixed-size microaggregation consists of taking fixed-size microaggregates (size k). Data-oriented microaggregation (with variable group size) was introduced recently. Regardless of the group size, microaggregations on a multidimensional...

Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery

Mohammed Khabzaoui, Clarisse Dhaenens, El-Ghazali Talbi (2008)

RAIRO - Operations Research

An important task of knowledge discovery deals with discovering association rules. This very general model has been widely studied and efficient algorithms have been proposed. But most of the time, only frequent rules are seeked. Here we propose to consider this problem as a multi-objective combinatorial optimization problem in order to be able to also find non frequent but interesting rules. As the search space may be very large, a discussion about different approaches is proposed and a hybrid...

Formalization of the Advanced Encryption Standard. Part I

Kenichi Arai, Hiroyuki Okazaki (2013)

Formalized Mathematics

In this article, we formalize the Advanced Encryption Standard (AES). AES, which is the most widely used symmetric cryptosystem in the world, is a block cipher that was selected by the National Institute of Standards and Technology (NIST) as an official Federal Information Processing Standard for the United States in 2001 [12]. AES is the successor to DES [13], which was formerly the most widely used symmetric cryptosystem in the world. We formalize the AES algorithm according to [12]. We then verify...

Privacy homomorphisms for statistical confidentiality.

Josep Domingo i Ferrer (1996)

Qüestiió

When publishing contingency tables which contain official statistics, a need to preserve statistical confidentiality arises. Statistical disclosure of individual units must be prevented. There is a wide choice of techniques to achieve this anonymization: cell supression, cell perturbation, etc. In this paper, we tackle the problem of using anonymized data to compute exact statistics; our approach is based on privacy homomorphisms, which are encryption transformations such that the decryption of...

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