Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals.We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Ubiquitous technologies create personal metadata on a very large scale. Our smartphones, browsers, cars, or credit cards generate infor-

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action.

Abstract-While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly concentrated on the use of ontologies to specify the semantics of activities, and ontological reasoning to recognize them based on context information. However, at the time of writing, the experimental evaluation of these techniques is limited to computational aspects; their actual effectiveness is still unknown. As a first step to fill this gap, in this paper, we experimentally evaluate the effectiveness of the ontological approach, using an activity dataset collected in a smart-home setting. Preliminary results suggest that existing ontological techniques underperform data-driven ones, mainly because they lack support for reasoning with temporal information. Indeed, we show that, when ontological techniques are extended with even simple forms of temporal reasoning, their effectiveness is comparable to the one of a state-ofthe-art technique based on Hidden Markov Models. Then, we indicate possible research directions to further improve the effectiveness of ontology-based activity recognition through temporal reasoning.

Cyber-security systems, which protect networks and computers against cyber attacks, are becoming common due to increasing threats and government regulation. At the same time, the enormous amount of data gathered by cyber-security systems poses a serious threat to the privacy of the people protected by those systems. To ground this threat, we survey common and novel cyber-security technologies and analyze them according to the potential for privacy invasion. We suggest a taxonomy for privacy risks assessment of information security technologies, based on the level of data exposure, the level of identification of individual users, the data sensitivity and the user control over the monitoring, and collection and analysis of the data. We discuss our results in light of the recent technological trends and suggest several new directions for making these mechanisms more privacy-aware.