Build Digital Democracy

Size: px

Start display at page:

Download "Build Digital Democracy"

Dennis Shelton
6 years ago
Views:

1 Build Digital Democracy By citizens, for citizens Dr

2 New Emerging Challenges

3 Big Data

4 Big Data Existing social mining practices threaten social cohesion surveillance has become increasingly privatized, commercialized and participatory, Julie E. Cohen

5 Build Digital Democracy [1]

6 An Alternative `Big Data Paradigm Open & free services public good Truly decentralized How to design & build an alternative Big Data paradigm for a sustainable society? Accurate but privacy-preserving Participatory Business opportunities

7 An Outline Alternative Big Data System Participatory & truly decentralized 1. Self-determined Choices 2. Self-regulatory Information Sharing Internet of Things sensors, controllers, actuators 4. Collective Public Good Knowledge 3. Decentralized Data Analytics

8 1. Self-Determined Choices [2]

9 Nervousnet 1.0 Beta

Nervousnet 1.0 Beta Released beginning of June Android: https://play.google.com/store/apps/details?id=ch.ethz.soms.nervousnet&hl=fi ios: https://itunes.apple.com/us/app/nervousnet/id1000599804?

10 Nervousnet 1.0 Beta Released beginning of June Android: ios: Open & free! Source code in Github Functionality Sensor data storage on the phone (local) Real-time/query-based data visualization Sensor data storage on Nervousnet proxy (remote) Controllability of data logging/sharing Self-determination of Nervousnet proxy Controllability of data sampling

11 User Groups End-user What Nervousnet has to offer? Developer Researcher

12 Why to use Nervousnet Flexible infrastructure-independent deployment of social experiments Researcher Testbed platform for studying real-world distributed systems Large-scale spatio-temporal data Data to validate & test applications Open-source guiding examples Developer Extensible data-driven API for the development of virtual sensors Data visualization Awareness of own social behavior Virtual Sensors End user Privacy-preserving access to local sensor data Analytics on local sensor data

13 2. Self-Regulatory Information Sharing [3]

Privacy-preservation More data, more information, more knowledge, more security, more business opportunities, more

14 A Paradox in Digital Democracy Contradictions Fragmentation polarization, deceived citizens, social injustice, Big Data Analytics vs. Privacy-preservation More data, more information, more knowledge, more security, more business opportunities, more prosperity Less data, less information, less surveillance, less discrimination, more freedom/ justice, more social cohesion, more prosperity Research communities cancel out their contributions How to cancel out their paradox?

15 Information Sharing Low High privacy-preservation rewards High Low Data Collection Raw Data Sharing Regulation High Citizen Selections accuracy in analytics Low Citizen 1 High rewards costs Low Data Collection Data Collection Raw Data. Raw Data Sharing Regulation Citizen 2 Sharing Regulation Aggregator Selections Summarized Data Analytics Rewards Incentivization Citizen N Supply Demand

16 Privacy-preserving Information Sharing Summarized data s i,e,t, s i,1,c,, s i,2,c,, s i,l,c, Epochs e e=1 k i,1 e=2 k i,2 e=l k i,l Raw data r i,e,t r i,1,1, r i,1,2,, r i,1,t r i,2,1, r i,2,2,, r i,2,t r i,l,1, r i,l,2,, r i,l,t Epochs e e=1 e=2 e=l Raw data r i,e,t r i,1,1, r i,1,2,, r i,1,t Agent i e.g. 30 mins data for a year T=

17 Trade-off Measurements Accuracy in data analytics local vs. global relative error 1. Total data consumer budget 2. Citizen selections 3. Fair budget distribution 4. Incentives to steer information sharing Rewards 1. Summarization 2. Entropy 3. Diversity Privacy-preservation

18 Implementation Unsupervised learning Several implementation algorithms Clustering Semi-automated Empirical Fully-automated, data-driven Customizable number of clusters Manual - unbounded Smart Grid 6435 participants 1 sensor Datasets Nervousnet 154 participants several sensors Survey questions Privacy preferences Mapping survey answers to a summarization range

19 Privacy-preservation Smart Grid 8 Summarization Data - Daily Summarization Data - Weekly Raw Data - Weekly Raw Data - Daily Entropy 4 Diversity Summarization Data - Daily Summarization Data - Weekly Raw Data - Daily Raw Data - Weekly Summarization [%] Summarization [%]

20 Privacy-preservation - Nervousnet Accelerometer.X Accelerometer.Y Accelerometer.Z Battery Light Noise Accelerometer.X Accelerometer.Y Accelerometer.Z Battery Light Noise Entropy 0.6 Diversity Number of Participating Citizens [%] Number of Participating Citizens [%]

21 Data Analytics Accuracy Smart Grid 0.04 Daily Weekly 0.04 Daily Weekly Average Local Error 0.02 Global Error Number of Participating Citizens [%] Number of Participating Citizens [%]

22 Data Analytics Accuracy Nervousnet Accelerometer.X Accelerometer.Y Accelerometer.Z Battery Light Noise 4 Accelerometer.X Accelerometer.Y Accelerometer.Z Battery Light Noise 12 3 Average Local Error 10 8 Global Error Number of Participating Citizens [%] Number of Participating Citizens [%]

5 Average Linear Empirical Optimized Linear Optimized

23 Rewards Smart Grid Semi-automated Fully-automated data driven Rewards [ ] Average Linear Empirical Optimized Linear Optimized Summarization 0 (Anpassung im Folienmaster: Menü Summarization «Ansicht»[%] à «Folienmaster»)

24 Conclusions Higher summarization, higher privacy-preservation More participants, higher privacy-preservation From canceling out research contributions to canceling out their paradox Sensor types influence privacy-preservation & accuracy Local errors cancel out resulting in low global errors Incentivization can be optimized to be fair

25 An Outline Alternative Big Data System Participatory & truly decentralized 1. Self-determined Choices 2. Self-regulatory Information Sharing Internet of Things sensors, controllers, actuators 4. Collective Public Good Knowledge 3. Decentralized Data Analytics

26 3. Decentralized Data Analytics [4]

27 Data Analytics Common light-weight process for large-scale real-time analytics Aggregation functions, SUM, AVG, MAX, MIN, STDEV, COUNT, etc. Main component of the MapReduce model Privacy-preserving

28 The Aggregation Problem AVG=( )/5=3 Centralized Aggregator f() No duplicates 2 1 Updates

29 Dynamic Decentralized Aggregation Aggregation-function dependent Static Values Gossip-based Aggregation Tree-based Aggregation Synopsis Diffusion Routing-dependent Inaccuracies: Duplicate & outdated values

30 Research Question How to accurately compute in a decentralized fashion aggregation functions under a varying input sample?

31 DIAS Computes almost any aggregation function without changing a single line of code Gossip-based routing decoupled from aggregation DIAS The Dynamic Intelligent Aggregation Service In-network aggregation Every participating node contributes input & receives output Aggregates are accurately updated (i) under a varying input sample or (ii) when nodes join/leave the network Double-counting is prevented

32 Aggregation Model Disseminators Aggregators Push A=f(X i ) A D X i Pull Aggregates Possible states summarization clusters Bloom filters Efficient distributed memory system

33 Privacy by Design Aggregated possible states are summarized data instead of raw data Privacy for Free! Bloom filter information is implicit and not explicit - membership Join & leaves are handled by only using bloom filters no other information leak

34 Privacy by Design Synchronous Synchronous Asynchronous

35 Towards an Operational System Real-world prototype in Euler HPC at ETH

36 An Outline Alternative Big Data System Participatory & truly decentralized 1. Self-determined Choices 2. Self-regulatory Information Sharing Internet of Things sensors, controllers, actuators 4. Collective Public Good Knowledge 3. Decentralized Data Analytics

37 4. Collective Public Good Knowledge

38 Applications Privacy-preserving localization & navigation Mobility pattern recognition Earthquake detection Evacuation systems Ambient assisting living

39 Privacy-preserving Localization CCC

40 Final Conclusion An alternative more ethical & democratic Big Data paradigm is required Unleashing force: systems truly decentralized & privacy-preserving by design Long-term sustainable participation, social justice, social cohesion Citizen in the loop: Digital democracy by citizens for citizens

41 Announcements & Remarks

Research Hubs As coordinator of the programme Engineering Social Technologies for a Responsible Digital Future, Helbing, who is also the initiator of Nervousnet and the coordinator of FuturICT, will

42 Research Hubs As coordinator of the programme Engineering Social Technologies for a Responsible Digital Future, Helbing, who is also the initiator of Nervousnet and the coordinator of FuturICT, will hire ten PhD candidates to work actively in this area. im Folienmaster: Menü «Ansicht» à (Anpassung «Folienmaster»)

43 DSS Workshop 2 nd International Workshop on Data-driven Self-regulating Systems

44 Demos

Swarmpulse https://www.youtube.com/watch?

45 Swarmpulse

46 Nervousnet Competition Game

Questions? nervousnet.ethz.ch http://www.sobigdata.eu http://www.swarmpulse.

33-34, 2015 Macmillan Publishers Limited [2], Izabela Moise and Dirk Helbing, Privacy-preserving Ubiquitous

Conference on Advanced Information Networking and Applications-AINA-2015, pages 332-338, Gwangju, South Korea,

IEEE [3], Jovan Nikolic, Pablo Velasquez, Marcello Trovati, Nik Bessis and Dirk Helbing, Selfregulatory

47 Questions? nervousnet.ethz.ch [1] Dirk Helbing and, Build Digital Democracy, Nature, Vol. 527, pp , 2015 Macmillan Publishers Limited [2], Izabela Moise and Dirk Helbing, Privacy-preserving Ubiquitous Social Mining via Modular and Compositional Virtual Sensors, in the proceedings of the 29th IEEE International Conference on Advanced Information Networking and Applications-AINA-2015, pages , Gwangju, South Korea, March IEEE [3], Jovan Nikolic, Pablo Velasquez, Marcello Trovati, Nik Bessis and Dirk Helbing, Selfregulatory Information Sharing in Participatory Social Sensing, submitted [4], 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 epournaras@ethz.ch

Participatory Sharing Economies via Decentralized Internet of Things

Participatory Sharing Economies via Decentralized Internet of Things Evangelos Pournaras www.evangelospournaras.com Dr. Evangelos Pournaras May 2017 1 Motivation www.evangelospournaras.com smart cities