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Bachelor Thesis: Social Profile Exchanges in Proximity-based Mobile Social Networks


Social Profile Exchanges in Proximity-based Mobile Social Networks


Centralized Online Social Network (OSN) platforms suffer from major privacy concerns of users who have to entrust a lot of private data to one single service provider. One advantage of such a centralized architecture is that it is easy for the service provider to conduct profile matching and suggest similar users as new contacts. In peer-to-peer based Online Social Networks, privacy concerns are tackled. Recent developments in sensor technologies and web services make the smartphone the optimal social networking device: it typically has only one user, is highly personalized, and contains location and other context data about the user. Additionally to explicitly expressed friendship connections with other users, similar users – in terms of some profile feature, e.g., taste in music – can be found and connected to in a different layer of the social graph. To address privacy concerns, utilizing P2P technology for a distributed OSN, the social graph – indicating the connections between users – can be stored in a distributed manner, storing information about A’s contacts on A’s device. In order to extend the graph, new users that are similar should be added to A’s connections. In order to achieve this, in a proximity-based device-to-device scenario, users can share their location and context data to estimate their similarity. Psychological research has shown that people tend to connect with each other when they are similar to each other. In proximity, two users should be able to exchange data to determine their similarity. In this Bachelor’s thesis, an existing Android application prototype will be extended to facilitate the exchange of social profile data between two users via proximity-based wireless communication interfaces (e.g., NFC, Bluetooth, Wifi Direct) in order to enable the creation of social graphs in proximity-based mobile social networks. The existing Android prototype collects lots of different data from the smartphone, for example music listened to, location history, used apps, or amount of pictures taken. You will develop a meaningful social profile out of the available data and implement the wireless exchange of such profile data with a calculation of an estimation of two users’ similarity. For the different profile features, appropriate data structures should be chosen and implemented, this includes the one-hash Counting Bloom Filter researched for similarity estimation of multisets, see second reference below. The work on this thesis includes the following tasks:

  • Provide an overview of social profiles and propose a social profile out of the data the app collects.
  • Provide an overview of proximity-based data exchange possibilities.
  • Extend the existing Android prototype and implement the creation of a social profile utilizing appropriate data structures; including an implementation of the Counting Bloom Filter like described in the second reference.
  • Evaluation of the implementation with test data

Supervisor: Felix Beierle

Type:  Bachelor Thesis

Duration: 4 months

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TU Berlin - Service-centric Networking - TEL 19
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10587 Berlin, Germany
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