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In this paper, we analyze the data extracted from several open source software repositories. We observe that the change data follows a Zipf distribution. Based on the extracted data, we then develop three probabilistic models to predict which files will have changes or bugs. The first model is Maximum Likelihood Estimation (MLE), which simply counts the number of events, i.e., changes or bugs, that happen to each file and normalizes the counts to compute a probability distribution. The second model is Reflexive Exponential Decay (RED) in which we postulate that the predictive rate of modification in a file is incremented by any modification to that file and decays exponentially. The third model is called RED-Co-Change. With each modification to a given file, the RED-Co-Change model not only increments its predictive rate, but also increments the rate for other files that are related to the given file through previous co-changes. We then present an information-theoretic approach to evaluate the performance of different prediction models. In this approach, the closeness of model distribution to the actual unknown probability distribution of the system is measured using cross entropy. We evaluate our prediction models empirically using the proposed information-theoretic approach for six large open source systems. Based on this evaluation, we observe that of our three prediction models, the RED-Co-Change model predicts the distribution that is closest to the actual distribution for all the studied systems.

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Published data is prone to privacy attacks. Sanitization methods aim to prevent these attacks while maintaining usefulness of the data for legitimate users. Quantifying the trade-off between usefulness and privacy of published data has been the subject of much research in recent years. We propose a pragmatic framework for evaluating sanitization systems in real-life and use data mining utility as a universal measure of usefulness and privacy. We propose a definition for data mining utility that can be tuned to capture the needs of data users and the adversaries' intentions in a setting that is specified by a database, a candidate sanitization method, and privacy and utility concerns of data owner. We use this framework to evaluate and compare privacy and utility offered by two well-known sanitization methods, namely k-anonymity and -differential privacy, when UCI's "Adult" dataset and the Weka data mining package is used, and utility and privacy measures are defined for users and adversaries. In the case of k-anonymity, we compare our results with the recent work of Brickell and Shmatikov (KDD 2008), and show that using data mining algorithms increases their proposed adversarial gains.