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Bachlor Thesis: Automated Social Profile Generation
Automated Social Profile Generation
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 friends. 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 Device-to-Device scenario, users can share their location and context data to calculate or estimate their similarity.
In this Bachelor’s thesis, the idea of automatically building a profile about the user of a smartphone is followed. Visited locations, music listened to, weather, activity, or installed applications should be tracked and stored locally. Strategies for measuring such contexts and appropriate, compact data structures for exchanging data in Device-to-Device scenarios are to be researched in this work.
The goal of this thesis is to develop an Android application that demonstrates the functionalities of automatic social profiling and preparing data for Device-to-Device transfer. This includes the following tasks:
- Provide an overview on the state of the art of appropriate location and context measuring strategies and data structures for the given scenario.
- Give an overview of existing frameworks from this field.
- Extend the existing Android prototype and implement unobtrusive automatic social profiling of the smartphone user, aggregation of the collected data, and transfer in Device-to-Device scenarios.
- Evaluation of the system’s performance with regard to resource efficiency.
Supervisors: Felix Beierle, Kai Grunert
Type: Bachelor Thesis
Duration: 4 months
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