Welderufael B. Tesfay
KDDI R&D Laboratories and Inc., Japan
Goethe University Frankfurt, Germany
Personalised Privacy Preferences, Privacy by Default, Privacy by Design, Privacy Settings, Support Vector Machines, Clustering.
Information and Systems Security
Privacy Enhancing Technologies
In this paper, we present a novel mechanism that provides individuals with personalised privacy by default setting when they register into a new system or service. The proposed approach consists of an intelligent mechanism that learns users’ context and preferences to generate personalised default privacy settings. To achieve this, we used a machine learning approach that requires a minimal number of questions at the registration phase, and, based on users’ responses, sets up privacy settings associated to users’ privacy preferences for a particular service. This is the first attempt to predict general privacy preferences from a
minimal number of questions. We propose two approaches. The first scheme is based on the sole use of SVM to predict users’ personalised settings. The second scheme implemented an additional layer that includes clustering. The accuracy of proposed approaches is evaluated by comparing the guessed answers against the answers from a questionnaire administered to 1
0,000 participants. Results show that, the SVM based scheme is able to guess the the full set of personalised privacy settings with an accuracy of 85%, by using a limited input of only 5 answers from the user.