Privacy-preserving Ubiquitous Social Mining via Modular and Compositional Virtual Sensors

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1 Privacy-preserving Ubiquitous Social Mining via Modular and Compositional s Evangelos Pournaras, Iza Moise, Dirk Helbing (Anpassung im Folienmaster: Menü «Ansicht» à «Folienmaster») ((Vorname Nachname)) 22/03/15 1

Moise, Dirk Helbing (Anpassung im Folienmaster: Menü

2 Motivation Existing social mining practices threaten social cohesion surveillance has become increasingly privatized, commercialized and participatory, Julie E. Cohen

3 Research Question How to design an open, decentralized, privacy-preserving & participatory system to provide ubiquitous social mining services engineered as public good Social mining: the process of discovering information from data sensed in one or more social environments so that a social phenomenon is understood or a societal problem is tackled.

good Social mining: the process of discovering information from data sensed in one or

4 Approach Introduction of a modular & compositional approach so that citizens participate during building lifecycle & later on Why? Designing extensible & reusable social mining processes via compositional data flow of sensors simplifies application development A design principle with a potential to simplify crowd-sourcing activities & increase engagement of building communities.

Designing extensible & reusable social mining processes via compositional data flow of sensors

5 The Model Input Streams. Filter Output Stream. Environments Environment Aggregator

7 Portability Internet of Things We started in Android devices but we move to ios and later on other embedded platforms, e.g. Arduino

8 Planetary Nervous System User/Application Environment Android Android Environment Local Analytics Storage Environment ios ios Environment... Ubiquitous Ubiquitous Environment (Anpassung im Folienmaster: Menü «Ansicht» à «Folienmaster») Sharing Global Analytics Sharing Environment Network Environment

.. Ubiquitous Ubiquitous Environment (Anpassung im Folienmaster: Menü

9 Self-determination of Privacy Two privacy levels! 1. Local storage Filter of Android/iOS 2. Sharing Sharing

10 Local & Global Analytics Application Ecosystem Global Analytics Local Analytics Networking Environment Storage Environment Planetary Nervous Middleware System API Services

11 Local & Global Analytics Local analytics: Building new virtual sensors, e.g. noise sensor A data-driven API for social mining Global analytics: Building decentralized aggregation services DIAS Dynamic Intelligent Aggregation Service Evangelos Pournaras, Martijn Warnier and Frances M.T. Brazier, A Generic and Adaptive Aggregation Service for Large-scale Decentralized Networks, Complex Adaptive Systems Modeling, 1:19, 2013 SpringerOpen

noise sensor A data-driven API for social mining Global analytics: Building decentralized aggregation

12 Applications Real-time noise monitoring in Smart Cities Privacy-preserving social networking and navigation

13 Demonstration

14 Computational Results How to access the cost-effectiveness of the nervous system? Emulation! Data generation: 2 phone users, 20 virtual sensors, log every 5 sec, 10 days What are the storage, retrieval and battery consumption costs? 600 Insertion Time-Samsung Galaxy S II Retrieval Time-Samsung Galaxy S II Insertion Time-LG Nexus 5 Retrieval Time-LG Nexus 5 1 Samsung Galaxy S II LG Nexus Time (sec) Battery level Day Day

15 Conclusions Incremental development of social mining application sensors: a promising design approach for building ubiquitous social mining services that are by design open, decentralized, privacy-preserving & participatory Modular and compositional approach: stimulates engagement & innovation in crowd-sourcing activities Performance evaluation confirms the feasibility of the introduced model

privacy-preserving & participatory Modular and compositional approach: stimulates engagement &

16 Join our Collective Vision! Questions? https://github.com/mosgap/nervous

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