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#CeDEM2017 Smart Cities of Self-Determined Data Subjects

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The focus of the presentation was on describing the process from a data consumer to an analytics provider from step to step.

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#CeDEM2017 Smart Cities of Self-Determined Data Subjects

  1. 1. Berner Fachhochschule | Wirtschaft, Gesundheit, Soziale Arbeit Smart Cities of Self-Determined Data Subjects (SDDS) Graphic source: https://bam.files.bbci.co.uk Jan Frecè & Thomas Selzam Bern University of Applied Sciences, E-Government-Institute 17 May 2017, Danube University, Krems, Austria
  2. 2. Bern University of Applied Sciences | Department of Business, Health & Social Work 1. The Problem and its Resolution 2. Layers of the SDDS Approach 3. Layers at Work 4. Case Aftermath & Feature Overview Agenda 2
  3. 3. Bern University of Applied Sciences | Department of Business, Health & Social Work The Smart City Data Problem 3 Graphic sources: http://www.eoi.es, https://flaticon.com (Made by Freepik & Alfredo Hernandez) The more data  the better the city modeling  the smarter the city The more data  the better the citizen modeling  the smaller the individual privacy
  4. 4. Bern University of Applied Sciences | Department of Business, Health & Social Work ▶ All personal data is stored in decentralized data stores, where it emerges. ▶ The functions for data storage, assembly, analysis and finally consummation are logically separated. ▶ No unencrypted information and no personal information leave the data store. ▶ The only one with access to analysis results is the data consumer. Solving the Dilemma Using Self-Determined Data Subjects (SDDS) 4
  5. 5. Bern University of Applied Sciences | Department of Business, Health & Social Work The Layers of an SDDS approach 5 Data Layer [containing all unencrypted personal data stored and managed in decentralized data storages] Assembly Layer [containing combined, encrypted and de-personalized data sets from the data layer] Analysis Layer [containing encrypted data from the assembly layer, the algorithms to analyze this data and the encrypted results stemming from the analysis] Consumer Layer [containing encrypted analysis results from analysis layer, able to decrypt the results]
  6. 6. Bern University of Applied Sciences | Department of Business, Health & Social Work Peter wants to support the city by providing his transportation data, but he does not want to reveal information younger than two weeks and no information from Wednesdays. Use Case Setup I 6Graphic source: Made by Freepik on https://flaticon.com The City Department of Transportation is interested to know which means of transportation people have used at which times of day, for what distances, in the last three months.
  7. 7. Bern University of Applied Sciences | Department of Business, Health & Social Work Use Case Setup II 7Graphic source: Made by Freepik on https://flaticon.com A tracker in Peter’s car saves its movements and at home moves the data to Peter’s decentralized data store. Peter Muster, 2017 Yearly Subscriber City Department of Public Transportation All data from using public transport is saved on Peter’s subscription card and at home moved to Peter’s decentralized data store.
  8. 8. Bern University of Applied Sciences | Department of Business, Health & Social Work Data Announcement Overview 8Graphic source: https://www.omakpac.org Step 1: The data subject (Peter) authorizes the data creators (Public Transportation Card & Car GPS Sensor). Step 2: The data creators announce the data to the local SDDS node. Step 3: The local SDDS node creates an entry in the distributed ledger (block chain), thereby announcing the data’s existence. Step 4: Now the data subject can log into the SDDS platform and enter its access conditions. Only then the data becomes available.
  9. 9. Bern University of Applied Sciences | Department of Business, Health & Social Work Data Announcement Details Behind the Curtains 9Graphic source: https://www.omakpac.org Data is announced • in an SDDS block chain, as reference only, • with an encrypted owner ID, • with an encrypted location ID, • with an unencrypted data type identifier, • in connection with smart contracts, enforcing the access conditions. These smart contracts are the only gateway to reach the decentralized stores.
  10. 10. Bern University of Applied Sciences | Department of Business, Health & Social Work Data Analysis: A Few More Details 10 Step 1: The data consumer (Department of Transportation) creates a new information request at the SDDS platform. Step 2: The platform isolates the entries in the distributed ledger (block chain) using the Type-ID and triggers the associated smart contracts. Step 3: If all access conditions (older than two weeks, no Wednesdays) are met, the local SDDS node is contacted (through a anonymization layer). Step 4: The distributed data store extracts the demanded data, removes personal information and forwards it to the local SDDS node.
  11. 11. Bern University of Applied Sciences | Department of Business, Health & Social Work Data Analysis: A Few More Details 11 Step 5: The local SDDS node encrypts the data for analyzing and forwards it to the SDDS platform. Step 6: The SDDS platform assembles the data from all local nodes and forwards it to the Analytics Provider. Step 7: The Analytics Provider executes the selected analytic algorithm upon the encrypted data, producing an encrypted result. Step 8: The data consumer can download and decrypt the result.
  12. 12. Bern University of Applied Sciences | Department of Business, Health & Social Work Data Analysis: The SDDS Layers at Work 12 Data Layer Assembly Layer Analysis Layer Consumer Layer Graphic source: Made by Freepik, Macrovector and Plainicon on https://flaticon.com
  13. 13. Bern University of Applied Sciences | Department of Business, Health & Social Work Use Case Scenario – Aftermath I 13Graphic source: Made by Freepik on https://flaticon.com • Peter could put his private data source to use and help his city. Possibly, he is even remunerated for his service. • No personal data has been revealed. • No data has been revealed in general, only information provided. • SDDS Platform does not store any data, only IAM information. • No access to analysis results. • Only anonymized, encrypted data is processed. • The location and the creator of the data to be analyzed remains unknown. • Only references are saved in block chain  no data exposure in the case of encryption withering away.
  14. 14. Bern University of Applied Sciences | Department of Business, Health & Social Work Use Case Scenario – Aftermath II 14Graphic source: Made by Freepik on https://flaticon.com • As desired, the Department of Transportation has information concerning which means of transportation are used for what kind of distances at which time of day. • Only relevant data (of the last three months) was processed. • No raw data has been revealed, only agreed information. Sample result: 14-15h: Distance < 1km  22.3% City Bus • No location data or names have been revealed  no risk of mishandling personal data.
  15. 15. Bern University of Applied Sciences | Department of Business, Health & Social Work SDDS Main Features 15 • All data remains where it has been created. Outside of decentralized data stores, only references are saved. • Only encrypted, de-personalized excerpts leave the data store. • Data subject decides what to share under which conditions. • Information can be shared without revealing the actual data, nor the data creator. • Using Proxy Re-encryption, the SDDS platform prevents itself from being able to decrypt data or results and still processes them. • All roles are cryptographically isolated. Even double roles are possible, e.g. data consumer can also be an analytics provider without revealing more data.
  16. 16. Bern University of Applied Sciences | Department of Business, Health & Social Work Thank you for your attention! Do you have any questions? 16 Jan Frecè & Thomas Selzam Bern University of Applied Sciences E-Government-Institute jan.frece@bfh.ch thomas.selzam@bfh.ch

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