A quantitative and qualitative analysis of blocking in association rule hiding

Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking this into consideration, we study the procedure of hiding sensitive association rules in binary data sets by blocking some data values and we present an algorithm for solving this problem. We also provide a fuzzification of the support and the confidence of an association rule in order to accommodate for the existence of blocked/unknown values. In addition, we quantitatively compare the proposed algorithm with other already published algorithms by running experiments on binary data sets, and we also qualitatively compare the efficiency of the proposed algorithm in hiding association rules. We utilize the notion of border rules, by putting weights in each rule, and we use effective data structures for the representation of the rules so as (a) to minimize the side effects created by the hiding process and (b) to speed up the selection of the victim transactions. Finally, we discuss the advantages and the limitations of blocking. Copyright 2004 ACM

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We present automated, real-time models built with machine learning algorithms which use videotapes of subjects’ faces in conjunction with physiological measurements to predict rated emotion (trained coders ’ second-by-second assessments of sadness or amusement). Input consisted of videotapes of 41 subjects watching emotionallyevocative films along with measures of their cardiovascular activity, somatic activity, and electrodermal responding. We built algorithms based on extracted points from the subjects ’ faces as well as their physiological responses. Strengths of the current approach are 1) we are assessing real behavior of subjects watching emotional videos instead of actors making facial poses, 2) the training data allow us to predict both emotion type (amusement versus sadness) as well as the intensity level of each emotion, 3) we provide a direct comparison between person-specific, gender-specific, and general models. Results demonstrated good fits for the models overall, with better performance for emotion categories than for emotion intensity, for amusement ratings than sadness ratings, for a full model using both physiological measures and facial tracking than for either cue alone, and for person-specific models than for gender-specific or general models. 1