Online Privacy as a Collective Phenomenon

The problem of online privacy is often reduced to individual decisions to hide or reveal personal information in online social networks (OSNs). However, with the increasing use of OSNs, it becomes more important to understand the role of the social network in disclosing personal information that a user has not revealed voluntarily: How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? Without strong technical effort, an OSN may be able to exploit the assortativity of human private features, this way constructing shadow profiles with information that users chose not to share. Furthermore, because many users share their phone and email contact lists, this allows an OSN to create full shadow profiles for people who do not even have an account for this OSN. We empirically test the feasibility of constructing shadow profiles of sexual orientation for users and non-users, using data from more than 3 Million accounts of a single OSN. We quantify a lower bound for the predictive power derived from the social network of a user, to demonstrate how the predictability of sexual orientation increases with the size of this network and the tendency to share personal information. This allows us to define a privacy leak factor that links individual privacy loss with the decision of other individuals to disclose information. Our statistical analysis reveals that some individuals are at a higher risk of privacy loss, as prediction accuracy increases for users with a larger and more homogeneous first- and second-order neighborhood of their social network. While we do not provide evidence that shadow profiles exist at all, our results show that disclosing of private information is not restricted to an individual choice, but becomes a collective decision that has implications for policy and privacy regulation

SCAMPI: Service platform for soCial Aware Mobile and Pervasive computIng

Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources asservice components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment

Privacy and Coordination: Computing on Databases with Endogenous Participation

We propose a simple model where individuals in a privacy-sensitive population decide whether or not to participate in a pre-announced noisy computation by an analyst, so that the database itself is endogenously determined by individuals’ participation choices. The privacy an agent receives depends both on the announced noise level, as well as how many agents choose to participate in the database. Each agent has some minimum privacy requirement, and decides whether or not to participate based on how her privacy requirement compares against her expectation of the privacy she will receive if she participates in the computation. This gives rise to a game amongst the agents, where each individual’s privacy if she participates, and therefore her participation choice, depends on the choices of the rest of the population. We investigate symmetric Bayes-Nash equilibria, which in this game consist of threshold strategies, where all agents whose privacy requirements are weaker than a certain threshold participate and the remaining agents do not. We characterize these equilibria, which depend both on the noise announced by the analyst and the population size; present results on existence, uniqueness, and multiplicity; and discuss a number of surprising properties they display