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Building Open Data Markets Using Sensing as a Service Model

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This presentation highlights some of the major challenges and research directions towards Building Open Data Markets Using Sensing as a Service Model

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Building Open Data Markets Using Sensing as a Service Model

  1. 1. Charith Perera ( Building Open Data Markets Using Sensing as a Service Model
  2. 2. Building [Open] Data Markets Using Sensing as a Service Model [Open] to see what is up for sale Does NOT mean free to access [Open] for any one to request • Raw Data Availability • Question Answerability
  3. 3. Building Open [Data Markets] Using Sensing as a Service Model Some people buy data Some people sell data Value Trading Smart Home Money Vouchers Other Types of gifts Points Actionable AdviceSmart City Smart Stadium / Arena Medical Research Government  Manufacturing Company   Electricity Company
  4. 4. Building Open Data Markets Using [Sensing as a Service Model] Everything as a Service (XaaS) = The acronym refers to an increasing number of services that are delivered over the Internet rather than provided locally or on-site. Request and Buy only what you need  No Pre packaged data
  5. 5. Why bother to create a Data Market For Data Owners = Rewards + Efficient life style For Data Consumers = Cost Reduction For Society = Resource Wastage Reduction through understanding (and predicting) ourselves and our surroundings better…
  6. 6. Will Monetizing Really Work Consumer Surveys Start-up Trends Loyalty Programs
  7. 7. Google Opinion Rewards Survey Monkey
  8. 8. What is the Main Problem Now No one wants to give away their data unless they are satisfied that their privacy would protected at all times Satisfied = Perceived Reward > Perceived Risks
  9. 9. Overview of the Research Agenda • Privacy Preferences Modelling • Privacy Preferences Capturing and Profiling • Adaptive Privacy Preferences • Risk-Benefit Analysis and Presentation • Data Trading Negotiations • Privacy Preserving Data Analytics (For IoT Middleware) Building Open Data Markets Using Sensing as a Service Model
  10. 10. Privacy Preferences Modelling • How privacy has been modelled in the past in different contexts • How privacy knowledge has been used in different systems What are the common characteristics, features, strengths and weaknesses in past approaches • What can we learn from them (i.e. in term of both modelling and usage) • How can we, potentially, bring those past experiences into IoT
  11. 11. • Can we use existing privacy knowledge models to capture privacy preferences in IoT domain • If not, why existing knowledge models are not sufficient • Why privacy Knowledge is important in data trading • How can we model privacy knowledge comprehensively that would be ‘ideal’ for IoT domain, specially towards data trading • What are the most important factors that need to be captured in order to model stakeholders’ privacy preference Privacy Preferences Modelling
  12. 12. • How to acquire privacy preferences, to be modelled using the privacy ontology, from users without overloading them • How to build unique privacy profiles for each user • What kind of interaction techniques can be used to acquire user inputs • What factors can be easily predicted and difficult to predict • Can we used recommendation engine to build a template for each user and then customize it further for unique personality Privacy Preferences Capturing and Profiling
  13. 13. • How privacy preferences of a data owner might change over time • How to handle adding/removing of new IoT devices (== more types of data to trade) overtime • Can we help the data owners to configure their privacy preferences profiles over time • Can an intelligent system autonomously configure privacy preferences on behalf of data owners (== M2M) Privacy Preferences
  14. 14. • How to model Risks and Benefits in General and in different domains • What factors need to be considered? Frequency of data ? Granularity of data? Type of data? Data consumers reputation? Etc. • How to bring qualitative facts into quantitative domain • How to compute final recommendation and turning quantitative fact into qualitative recommendations • How to inform such risks and rewards in a useful and understandable manner to non-technical users so they can take informed decisions. Risk-Benefit Analysis and Presentation
  15. 15. • How to conduct data trading negotiation by considering both data owner’s and data consumer’s privacy preferences profiles • Can we completely automate (or Semi-autonomous) the negotiation process?? • What is the user involvement in data trading negotiations may look like • How an ideal data market may look like Data Trading Negotiations
  16. 16. • What are the main gaps in existing IoT middleware platforms specially from privacy perspective. • What are the most commonly used privacy preserving data analysis (PPDA) techniques • How do we built those PPDA into IoT middleware platforms • How do we combine multiple PPDAs to gather to protect data owners privacy? IoT Middleware for Privacy Preserving Data Analytics
  17. 17. • What kind of factors need to be considered when combining multiple PPDAs? Strengths ? Weaknesses? Limitations? Applicability ? • Can we develop an extensible framework so others can add new PPDAs to the system later on • How do we develop an intelligent system that considers all the exiting PPDAs + data owners privacy preferences + data consumer privacy preferences + other factors and develop unique configuration plan at runtime to facilitate privacy protection? IoT Middleware for Privacy Preserving Data Analytics