Affinity: A System for Latent User Similarity Comparison on Texting Data

Abstract

In the field of social networking services, finding similar users based on profile data is common practice. Smartphones harbor sensor and personal context data that can be used for user profiling. Yet, one vast source of personal data, that is text messaging data, has hardly been studied for user profiling. We see three reasons for this: First, private text messaging data is not shared due to their intimate character. Second, the definition of an appropriate privacy-preserving similarity measure is nontrivial. Third, assessing the quality of a similarity measure on text messaging data representing a potentially infinite set of topics is non-trivial. In order to overcome these obstacles we propose affinity, a system that assesses the similarity between text messaging histories of users reliably and efficiently in a privacypreserving manner. Private texting data stays on user devices and data for comparison is compared in a latent format that neither allows to reconstruct the comparison words nor any original private plain text. We evaluate our approach by calculating similarities between Twitter histories of 60 US senators. The resulting similarity network reaches an average 85.0% accuracy on a political party classification task.

MobRec: Mobile Platform for Decentralized Recommender Systems

“…Today, already more than half of the world's population lives in urban areas and the trend is that this percentage is increasing. 2 There are several things to consider when designing MobRec. In the following, we describe the technical (functional) requirements of our systems.…”

MobRec — Mobile Platform for Decentralized Recommender Systems

“…Such a system especially works in urban areas. Today, already more than half of the world's population lives in urban areas and the trend is that this percentage is increasing 2 . There are several things to consider when designing MobRec.…”

“…In [1], we developed and evaluated a method for estimating similarity based on users' context data using probabilistic data structures. In [2], we developed a privacy-preserving method for determining the similarity of two users based on their text messaging data. Both of those methods can be implemented in our proposed architecture to find similar users, without having the need to have users that rated the same items.…”

“…Finding similar users might be simple with smartphones: spending time at the same location might imply similarity -at least to a certain degree. Additionally, from our previous research, other methods of determining similarity between users based on smartphone data are available [1], [2]. Thus, exchanging data between smartphones in proximity in a device-to-device fashion allows to create local databases that allow to filter for similar users.…”

On gossip-based information dissemination in pervasive recommender systems

“…Similarity data gets propagated to nearby peers and therefore has to be privacy-preserving. Privacy-preserving similarity comparison can among others be performed on item vectors [4] as well as texting data [15]. • Context Data: Data that characterizes the encounter such as location, time, weather, or peer activity (running, eating, commuting) that can be sensed (for example via sensors) or retrieved (for example from the web) [6,30].…”