Abstract-Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.

Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like socialnetworking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent results in terms of energy savings and user experience. This work has been performed in the framework of the FP7 project TROPIC IST-318784 STP, which is funded by the European Community. The Authors would like to acknowledge the contributions of their colleagues from TROPIC Consortium (http://www.ict-tropic.eu).

Commercial Virtual Private Network (VPN) services have become a popular and convenient technology for users seeking privacy and anonymity. They have been applied to a wide range of use cases, with commercial providers often making bold claims regarding their ability to fulfil each of these needs, e.g., censorship circumvention, anonymity and protection from monitoring and tracking. However, as of yet, the claims made by these providers have not received a sufficiently detailed scrutiny. This paper thus investigates the claims of privacy and anonymity in commercial VPN services. We analyse 14 of the most popular ones, inspecting their internals and their infrastructures. Despite being a known issue, our experimental study reveals that the majority of VPN services suffer from IPv6 traffic leakage. The work is extended by developing more sophisticated DNS hijacking attacks that allow all traffic to be transparently captured.We conclude discussing a range of best practices and countermeasures that can address these vulnerabilities

The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?".In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants-language, vendor adoption, and so on.

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