Potential mass surveillance and privacy violations in proximity-based social applications

Proximity-based social applications let users interact with people that are currently close to them, by revealing some information about their preferences and whereabouts. This information is acquired through passive geo-localisation and used to build a sense of serendipitous discovery of people, places and interests. Unfortunately, while this class of applications opens different interactions possibilities for people in urban settings, obtaining access to certain identity information could lead a possible privacy attacker to identify and follow a user in their movements in a specific period of time. The same information shared through the platform could also help an attacker to link the victim's online profiles to physical identities. We analyse a set of popular dating application that shares users relative distances within a certain radius and show how, by using the information shared on these platforms, it is possible to formalise a multilateration attack, able to identify the user actual position. The same attack can also be used to follow a user in all their movements within a certain period of time, therefore identifying their habits and Points of Interest across the city. Furthermore we introduce a social attack which uses common Facebook likes to profile a person and finally identify their real identity

Privacy Tradeoffs in Predictive Analytics

Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.Comment: Extended version of the paper appearing in SIGMETRICS 201

On the anonymity risk of time-varying user profiles.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version

Reducing Third Parties in the Network through Client-Side Intelligence

The end-to-end argument describes the communication between a client and server using functionality that is located at the end points of a distributed system. From a security and privacy perspective, clients only need to trust the server they are trying to reach instead of intermediate system nodes and other third-party entities. Clients accessing the Internet today and more specifically the World Wide Web have to interact with a plethora of network entities for name resolution, traffic routing and content delivery. While individual communications with those entities may some times be end to end, from the user's perspective they are intermediaries the user has to trust in order to access the website behind a domain name. This complex interaction lacks transparency and control and expands the attack surface beyond the server clients are trying to reach directly. In this dissertation, we develop a set of novel design principles and architectures to reduce the number of third-party services and networks a client's traffic is exposed to when browsing the web. Our proposals bring additional intelligence to the client and can be adopted without changes to the third parties. Websites can include content, such as images and iframes, located on third-party servers. Browsers loading an HTML page will contact these additional servers to satisfy external content dependencies. Such interaction has privacy implications because it includes context related to the user's browsing history. For example, the widespread adoption of "social plugins" enables the respective social networking services to track a growing part of its members' online activity. These plugins are commonly implemented as HTML iframes originating from the domain of the respective social network. They are embedded in sites users might visit, for instance to read the news or do shopping. Facebook's Like button is an example of a social plugin. While one could prevent the browser from connecting to third-party servers, it would break existing functionality and thus be unlikely to be widely adopted. We propose a novel design for privacy-preserving social plugins that decouples the retrieval of user-specific content from the loading of third-party content. Our approach can be adopted by web browsers without the need for server-side changes. Our design has the benefit of avoiding the transmission of user-identifying information to the third-party server while preserving the original functionality of the plugins. In addition, we propose an architecture which reduces the networks involved when routing traffic to a website. Users then have to trust fewer organizations with their traffic. Such trust is necessary today because for example we observe that only 30% of popular web servers offer HTTPS. At the same time there is evidence that network adversaries carry out active and passive attacks against users. We argue that if end-to-end security with a server is not available the next best thing is a secure link to a network that is close to the server and will act as a gateway. Our approach identifies network vantage points in the cloud, enables a client to establish secure tunnels to them and intelligently routes traffic based on its destination. The proliferation of infrastructure-as-a-service platforms makes it practical for users to benefit from the cloud. We determine that our architecture is practical because our proposed use of the cloud aligns with existing ways end-user devices leverage it today. Users control both endpoints of the tunnel and do not depend on the cooperation of individual websites. We are thus able to eliminate third-party networks for 20% of popular web servers, reduce network paths to 1 hop for an additional 20% and shorten the rest. We hypothesize that user privacy on the web can be improved in terms of transparency and control by reducing the systems and services that are indirectly and automatically involved. We also hypothesize that such reduction can be achieved unilaterally through client-side initiatives and without affecting the operation of individual websites

Social network data analysis to highlight privacy threats in sharing data

AbstractSocial networks are a vast source of information, and they have been increasing impact on people's daily lives. They permit us to share emotions, passions, and interactions with other people around the world. While enabling people to exhibit their lives, social networks guarantee their privacy. The definitions of privacy requirements and default policies for safeguarding people's data are the most difficult challenges that social networks have to deal with. In this work, we have collected data concerning people who have different social network profiles, aiming to analyse privacy requirements offered by social networks. In particular, we have built a tool exploiting image-recognition techniques to recognise a user from his/her picture, aiming to collect his/her personal data accessible through social networks where s/he has a profile. We have composed a dataset of 5000 users by combining data available from several social networks; we compared social network data mandatory in the registration phases, publicly accessible and those retrieved by our analysis. We aim to analyse the amount of extrapolated data for evaluating privacy threats when users share information on different social networks to help them be aware of these aspects. This work shows how users data on social networks can be retrieved easily by representing a clear privacy violation. Our research aims to improve the user's awareness concerning the spreading and managing of social networks data. To this end, we highlighted all the statistical evaluations made over the gathered data for putting in evidence the privacy issues