Personal Data Manager & Adaptive Inference Discovery Service

Project description

PDM/AID-S is a framework to support users to take informed decisions about privacy settings with IoT personal devices

The main objective is to protect users against inference attacks from trusted Third Parties.

The framework is composed of two core components, namely the Personal Data Manager (PDM) and the Adaptive Inference Discovery Service (AID-S).

PDM is responsible for managing the user’s privacy preferences and the interaction with the TPs, while AIDS computes potential inference risks given the combination of the user’s personal data that are requested by the TP. Also, PDM has the tasks of authorization, authentication and user profiling.

The request of personal data from the third party (in a form of Policy Statement) is regulated by the user's PDM before authorizing access. It checks if the Policy Statement is coherent with the user's privacy settings. The AID-S, on the other hand, computes the inference risks associated to data disclosure. This combination enables to ensure full protection of the user concerning privacy risks.

Current extension of the project: Automatic Modeling User Privacy Preferences
People: Bart Knijnenburg, Odnan Sanchez Ref, Ilaria Torre

Please, contribute to our research: fill in our crowdsourcing survey questionnaire



Publications:

  • Torre Ilaria, Sanchez Odnan Ref, Koceva Frosina, Adorni Giovanni (2017) Supporting users to take informed decisions on privacy settings of personal devices. PERSONAL AND UBIQUITOUS COMPUTING, p. 1-20, ISSN: 1617-4909, doi: 10.1007/s00779-017-1068-3
  • Torre Ilaria, Koceva Frosina, Sanchez Odnan Ref, Adorni Giovanni (2016) A Framework for Personal Data Protection in the IoT. In: Proceedings of the 11th International Conference for Internet Technology and Secured Transactions. p. 384-391, ISBN: 978-1-908320-74-2, Barcelona, Spain, 05/12/2016, doi: 10.1109/ICITST.2016.7856735
  • Torre Ilaria, Koceva Frosina, Sanchez Odnan, Adorni Giovanni (2016) Fitness Trackers and Wearable Devices: How to Prevent Inference Risks?. In: Proceedings of the 11th International Conference on Body Area Networks, BodyNets 2016. p. 1-8, ACM Digital Library, Turin, Italy, 15/12/2016, doi: 10.4108/eai.15-12-2016.2267791
  • Torre Ilaria, Adorni Giovanni, Koceva Frosina, Sanchez Odnan (2016) Preventing Disclosure of Personal Data in IoT Networks. In: Proc. of the 12th International Conference on Signal-Image Technology & Internet-Based Systems. p. 389-396, IEEE Computer Society, ISBN: 978-1-5090-5698-9, Naples, 28/11/2016, doi: 10.1109/SITIS.2016.68
For more information about this project, please contact Odnan Ref Sanchez, Ilaria Torre

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