Privacy Preserving, Standard- Based Wellness and Activity Data Modelling & Management within Smart Homes

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1 Privacy Preserving, Standard- Based Wellness and Activity Data Modelling & Management within Smart Homes Ismini Psychoula (ESR 3) De Montfort University Prof. Liming Chen, Dr. Feng Chen 24 th October 2017

2 Agenda! ESR Background! Research Objectives! Training Experience! Achievements

3 ESR Background! University of Western Macedonia Informatics & Telecommunications Engineering ( )! MIT Media Lab Visiting Student/Researcher ( )! Delft University of Technology MSc Computer Science ( )

4 Role in ACROSSING! ESR / PhD, September 2016

5 Personal Data at Risk Alarm " Location " Home empty Smart Refrigerator " Banking Details " Preferred Products " Location Data Smart Metering " Habits " Energy Consumption " Home empty Smart TV " Registration Details " Viewing Habits " Banking Details Heating " Habits " Energy Consumption Camera " Location " Video recordings " Empty Home " Guests Sleeping Mats " Location " Sleep Patterns " Empty Home Smart Watch " Location " Heart Rate " Exercise Habits Medicine Control " Medical History " Health at Risk " Life at Risk

6 Research Problems! Providing composable privacy solutions! Application Independency vs tailored privacy solutions! Systematic approach to preserving privacy! Privacy for the evolutionary sensing environments of Smart Homes! Evaluation of privacy risks! Trade off among privacy, performance and data fidelity! Anonymity versus data integrity! Multi-party privacy protection! Overriding privacy! Users not aware of the risks! Defining standards for privacy research! Open datasets! Open privacy solutions

7 Research Plan & Objectives 1 st Study -Collection of behavioral data -Privacy modelling of information in AAL -End users perspectives on privacy 3 rd Study -Privacy Management Framework -Privacy Assistant for privacy settings recommendations 2 nd Study -Privacy Risk measurement techniques (Functions of Likelihood and Impact) -Effect of privacy enhancing technologies (PETs) on these privacy risk metrics 4 th Study -Data Sharing Mechanisms -Modelling trade off between utility and privacy

8 Research Methodology! Review of available privacy preserving solutions for SH! Identification of existing requirements regarding privacy in SH! Design of research studies! Qualitative studies with end users! Development of methods to address the privacy requirements! Evaluation of the privacy solutions

9 Experimental Study! Great Northern Haven! 15 Residents! 2 Weeks

10 Experimental Study (2)! Focus groups with users! Extension of existing infrastructure! Privacy risk evaluation mechanism

11 ACROSSING Training Experiences Acquirement of a diverse set of skills by attending courses, trainings and summer schools Interaction with end users through secondments Networking with both industry and academia

other ESRs Best Student Paper Award [1] Ismini Psychoula, Liming Chen and Feng Chen. Privacy Modelling and Management for Assisted Living within Smart Homes.

[2] Sarah Fallmann, Ismini Psychoula, Liming Chen, Feng Chen, Julie Doyle and Darpan Triboan.

In 14th International Conference on Ubiquitous Intelligence and Computing (UIC), to be published, IEEE 2017 [3] Deepika Singh, Erinc Merdivan, Ismini Psychoula,

12 Research Achievements (2)!!!! Established understanding about the privacy topic in the AAL field Broaden research network by attending conferences Publications and collaborations with other ESRs Best Student Paper Award [1] Ismini Psychoula, Liming Chen and Feng Chen. Privacy Modelling and Management for Assisted Living within Smart Homes. In 19th International Conference on E-health Networking, Application & Services (Healthcom), to be published, IEEE [2] Sarah Fallmann, Ismini Psychoula, Liming Chen, Feng Chen, Julie Doyle and Darpan Triboan. Reality and Perception: Activity monitoring and data collection within a real-world smart home. In 14th International Conference on Ubiquitous Intelligence and Computing (UIC), to be published, IEEE 2017 [3] Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, and Andreas Holzinger. "Human Activity Recognition Using Recurrent Neural Networks". In International Cross-Domain Conference for Machine Learning and Knowledge Extraction, pp Springer, 2017 [4] Triboan D., Chen L., Chen F., Fallmann S., Psychoula I., Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments, Proceedings of the IEEE UIC2017 conference, August 2017, to appear

13 Impact on Future Career! Wider perception and research field! Insight into the privacy research community! Attending summer schools and workshops # Contact with lead researchers in the privacy field! Collaborations with other ESRs and partners # Increasing research network! Training events # Gain knowledge in different areas (sensors, context awareness, behavior analysis)! Improvement of research and scientific skills! Acquirement of different transferable skills! ESR Representative # Leadership, Management skills! Poster Chair # Organizational, Communication Skills

14 Thank you! Any Questions? Acknowledgement This research work is funded by the EU H2020 Marie Curie Actions Programme 14

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