Unilateral Invasions of Privacy

Most people seem to agree that individuals have too little privacy, and most proposals to address that problem focus on ways to give those users more information about, and more control over, how information about them is used. Yet in nearly all cases, information subjects are not the parties who make decisions about how information is collected, used, and disseminated; instead, outsiders make unilateral decisions to collect, use, and disseminate information about others. These potential privacy invaders, acting without input from information subjects, are the parties to whom proposals to protect privacy must be directed. This Article develops a theory of unilateral invasions of privacy rooted in the incentives of potential outside invaders. It first briefly describes the different kinds of information flows that can result in losses of privacy and the private costs and benefits to the participants in these information flows. It argues that in many cases the relevant costs and benefits are those of an outsider deciding whether certain information flows occur. These outside invaders are more likely to act when their own private costs and benefits make particular information flows worthwhile, regardless of the effects on information subjects or on social welfare. And potential privacy invaders are quite sensitive to changes in these costs and benefits, unlike information subjects, for whom transaction costs can overwhelm incentives to make information more or less private. The Article then turns to privacy regulation, arguing that this unilateral-invasion theory sheds light on how effective privacy regulations should be designed. Effective regulations are those that help match the costs and benefits faced by a potential privacy invader with the costs and benefits to society of a given information flow. Law can help do so by raising or lowering the costs or benefits of a privacy invasion, but only after taking account of other costs and benefits faced by the potential privacy invader

Statistical interaction modeling of bovine herd behaviors

While there has been interest in modeling the group behavior of herds or flocks, much of this work has focused on simulating their collective spatial motion patterns which have not accounted for individuality in the herd and instead assume a homogenized role for all members or sub-groups of the herd. Animal behavior experts have noted that domestic animals exhibit behaviors that are indicative of social hierarchy: leader/follower type behaviors are present as well as dominance and subordination, aggression and rank order, and specific social affiliations may also exist. Both wild and domestic cattle are social species, and group behaviors are likely to be influenced by the expression of specific social interactions. In this paper, Global Positioning System coordinate fixes gathered from a herd of beef cows tracked in open fields over several days at a time are utilized to learn a model that focuses on the interactions within the herd as well as its overall movement. Using these data in this way explores the validity of existing group behavior models against actual herding behaviors. Domain knowledge, location geography and human observations, are utilized to explain the causes of these deviations from this idealized behavior

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146