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FIA Dublin Presentations: Data driven services: Enabling Privacy and Personalisation for a Pervasive Social Networking System by Howard Williams
The document discusses enabling privacy and personalization for a pervasive social networking system. It proposes combining pervasive computing, which connects devices and services, with social networking, which connects people. Two key supporting processes are privacy and personalization, both of which are data-driven and rely on learning. The system uses personalized privacy policy negotiation to allow users to control what personal data is disclosed. It also personalizes services for individuals and communities using various learning mechanisms like rules and neural networks to adapt to users' needs.
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FIA Dublin Presentations: Data driven services: Enabling Privacy and Personalisation for a Pervasive Social Networking System by Howard Williams
- 1. Data driven services:Enabling Privacy and Personalisation for a Pervasive Social Networking System Howard Williams (Heriot-Watt Univ.)
- 2. • Pervasive computingis concerned with connecting and interacting with devices and services around one • Social networking is concerned with connecting and interacting with people Pervasive Social Computing Networking FIA conference Dublin 09/05/13 Introduction
- 3. • Pervasive computingis concerned with connecting and interacting with devices and services around one • Social networking is concerned with connecting and interacting with people Pervasive Social Networking • Aim of Societies is to combine the two. FIA conference Dublin 09/05/13 Introduction
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- 5. FIA conference Dublin 09/05/13 KeySupporting Processes Two key supporting processes for sharing info/services • Needed to support Third Party Apps • User Privacy • Personalisation • Both depend on Learning • All three are data driven
- 6. FIA conference Dublin 09/05/13 Privacy •Need to control who has access what info • Personal info (e.g. name, age, credit card) • + Context info (e.g. location, other co-located users, current task) • Societies approach based on Personalised Privacy Policy Negotiation
- 7. FIA conference Dublin 09/05/13 Privacy •Privacypolicy negotiation enables users to choose the personal data they wish to disclose to other users, to third party apps or to communities. •Approach adopted uses preferences to automate the privacy policy negotiation process. This benefits the user in several ways - – The user is relieved of the burden of configuring the details of their privacy policy manually – It helps the user to be consistent with the data they disclose as the same preference can be applied in more than one situation •Trust used to ensure data owner and data requestor adhere to terms of mutual data disclosure agreement
- 8. FIA conference Dublin 09/05/13 LOOP:While Agreement not reached Jack’s CSS Identity Management Privacy Protection 1. Request to use Abby’s Travel Agent Service 2. Forward Privacy Policy statement 3. Examine Privacy Policy, evaluate privacy preferences, produce privacy policy 4. Reply with terms and conditions 5. examine terms and conditions 6. Send new terms 7. Agreement 8. Acknowledge Trust Management Abby’s CSS Identity Management Privacy Protection Trust Management Personalised Privacy Policy Negotiation
- 9. FIA conference Dublin 09/05/13 Privacy •System is able to control what data is revealed to whom, based on user’s privacy preferences. • User can also control the level of detail for any individual data item, e.g. location (data obfuscation) • Also used for multi-identity management and selection.
- 10. FIA conference Dublin 09/05/13 Personalisation •The set of processes that adapt the behaviour of a system to take account of the needs and preferences of the individual user in the current context. • This includes: – Automatic triggering of services – Service selection (device, app, ..) – Initial parameters for service – Changes to parameters when context changes • Result will depend on user and current context (location, time, day…)
- 11. FIA conference Dublin 09/05/13 Personalisation Societiesuses several different mechanisms: • Rule-based user preferences • Neural network • Bayesian network • Context aware user intent (CAUI) models • User intent based on Conditional Random Fields (CRFs)
- 12. FIA conference Dublin 09/05/13 Personalisation •Outputs from different mechanisms are used to take final decision on personalisation action U IntentN. N.Rule Prefs Action Decision Maker Conflict Resoln
- 13. FIA conference Dublin 09/05/13 Personalisation Societiesis extending the notion of personalisation to include the needs and preferences of communities as well as individuals. Thus we also have: • Rule-based community preferences • Context aware Community intent (CACI) models
- 14. FIA conference Dublin 09/05/13 Learning •Various forms of learning needed to build up profile of user preferences, … • Need to monitor user actions and context and build up a cloudlet containing user history.
- 15. Thus the basiclearning cycle for a rule-based learning approach is as follows: FIA conference Dublin 09/05/13 Learning Rule-based Preferences
- 16. FIA conference Dublin 09/05/13 Learningfor Privacy By monitoring user’s behaviour with regard to data disclosure, privacy preferences can be learnt that can be used to automate the processes of privacy components, e.g. - negotiating privacy policies, - selecting an appropriate identity to represent the user, and - controlling access to personal data
- 17. FIA conference Dublin 09/05/13 Learningfor Communities • Preferences are also learnt for communities of users – Historic behaviour and context data is collected from all members of a community (according to member privacy settings) and fused together to create a single behaviour and context history for the entire community – The community history is processed offline by batch machine learning techniques to extract context-dependent “Community Preferences” which become associated with the related community – Individual (or new) community members can inherit all or part of the community preferences set to enhance their own preference set
- 18. FIA conference Dublin 09/05/13 Learningfor Communities Community Prefs Prefs Prefs Prefs Apply C45 Machine Learning algorithm New preferences User Preference Learning History History History History
- 19. FIA conference Dublin 09/05/13 Applicationdomains Trial on three domains • University students • Disaster Management • Enterprise
- 20. FIA conference Dublin 09/05/13 Conclusion •Personalisation and privacy are two data-driven processes that are essential in a pervasive social networking system. In turn they rely on learning – another data-driven process. • If an app wants to obtain info on the user, it must use PPPN to obtain permission. • If an app wants to use personalisation info (preferences or intent) it must do this via the personalisation APIs.
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