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Online social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. In this paper, we propose an effective, scalable inference attack for released social networking data to infer undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such an inference attack.

Abstract-Concept-evolution has recently received a lot of attention in the context of mining data streams. Conceptevolution occurs when a new class evolves in the stream. Although many recent studies address this issue, most of them do not consider the scenario of recurring classes in the stream. A class is called recurring if it appears in the stream, disappears for a while, and then reappears again. Existing data stream classification techniques either misclassify the recurring class instances as another class, or falsely identify the recurring classes as novel. This increases the prediction error of the classifiers, and in some cases causes unnecessary waste in memory and computational resources. In this paper we address the recurring class issue by proposing a novel "class-based" ensemble technique, which substitutes the traditional "chunk-based" ensemble approaches and correctly distinguishes between a recurring class and a novel one. We analytically and experimentally confirm the superiority of our method over state-of-the-art techniques.

Abstract-The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical to store and use all the historical data for training. Data streams also frequently experience concept-drift as a result of changes in the underlying concepts. However, another important characteristic of data streams, namely, concept-evolution is rarely addressed in the literature. Concept-evolution occurs as a result of new classes evolving in the stream. This paper addresses concept-evolution in addition to the existing challenges of infinite-length and concept-drift. In this paper, the concept-evolution phenomenon is studied, and the insights are used to construct superior novel class detection techniques. First, we propose an adaptive threshold for outlier detection, which is a vital part of novel class detection. Second, we propose a probabilistic approach for novel class detection using discrete Gini Coefficient, and prove its effectiveness both theoretically and empirically. Finally, we address the issue of simultaneous multiple novel class occurrence, and provide an elegant solution to detect more than one novel classes at the same time. We also consider feature-evolution in text data streams, which occurs because new features (i.e., words) evolve in the stream. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.