A well known tenet for ensuring unauthorized leaks of sensitive data such as passwords and cryptographic keys is to erase ("zeroize") them after their intended use in any program. Prior work on minimizing sensitive data lifetimes has focused exclusively on sequential programs. In this work, we address the problem of data lifetime minimization for concurrent programs. We develop a new algorithm that precisely anticipates when to introduce these erasures, and develop an implementation of this algorithm in a tool called DEICS. Through an experimental evaluation, we show that DEICS is able to reduce lifetimes of shared sensitive data in several concurrent applications (over 100k lines of code combined) with minimal performance overheads.
Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data and therefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE) is proposed for identifying and eliminating duplicates among records in dynamic query results. The idea of this paper is to adjust the weights of record fields in calculating similarities among records. Three classifiers namely weight component similarity summing classifier, support vector machine classifier and one class support vector machine classifier are iteratively employed with UDE where the first classifier utilizes the weights set to match records from different data sources. With the matched records as positive dataset and non duplicate records as negative set, the second classifier identifies new duplicates. Then, one-class support vector machine classifier is employed for further detecting the duplicates. The iteration stops when no duplicates can be identified. Thus, this paper takes advantage of dissimilarity among records from web databases and solves the online duplicate detection problem.
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