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Abstract  Microaggregation is a family of methods for statistical disclosure control (SDC) of microdata (records on individuals and/or companies), that is, for masking microdata so that they can be released while preserving the privacy of the underlying individuals. The principle of microaggregation is to aggregate original database records into small groups prior to publication. Each group should contain at least k records to prevent disclosure of individual information, where k is a constant value preset by the data protector. Recently, microaggregation has been shown to be useful to achieve k-anonymity, in addition to it being a good masking method. Optimal microaggregation (with minimum within-groups variability loss) can be computed in polynomial time for univariate data. Unfortunately, for multivariate data it is an NP-hard problem. Several heuristic approaches to microaggregation have been proposed in the literature. Heuristics yielding groups with fixed size k tends to be more efficient, whereas data-oriented heuristics yielding variable group size tends to result in lower information loss. This paper presents new data-oriented heuristics which improve on the trade-off between computational complexity and information loss and are thus usable for large datasets. [ABSTRACT FROM AUTHOR]

Evaluates the use of electrocardiography and programmed ventricular simulation in the identification of an individual patient at risk of ventricular tachyarrhymias after myocardial infarction. Likelihood of signal-averaging for the detection of late ventricular potentials to be promising technique for the identification of patients at risk of ventricular tachyarrhythmias; Consideration of late potentials as stronger predictor of ventricular tachycardia inducibility; Usefulness of both techniques as screening methods.