Why should semantic technologies pay more attention to privacy... and vice-versa
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Semantic technologies deal with machine-interpretable data representations but often overlook privacy issues. The document discusses how semantic web technologies like linked data and ontologies are used to represent personal data on the web in ways that can threaten user privacy without transparency. It presents several research projects that aim to enhance privacy and transparency by using semantic techniques to provide analytics dashboards showing how personal data is used, modeling social media privacy policies with ontologies, and ensuring diverse smart city data hubs consider privacy impacts.
Why should semantic technologies pay more attention to privacy... and vice-versa
- 1. Why should semantic technologies pay more attention to privacy... and vice-versa Mathieu d’Aquin @mdaquin
- 2. Semantic Technologies Are about data and knowledge representations to make them interpretable and usable by machines, across the web.
- 3. Linked Data
- 5. Even AT The OU data.open.ac.uk
- 6. Ontologies As shared representation of the meaning of the data.
- 7. The Semantic Web Data Linked Data Semantic Web
- 8. WHat does that has to do with Privacy?
- 9. Transparency and Privacy go well together
- 12. Using a Local logging Proxy
- 13. Using a Local logging Proxy d'Aquin, M., Elahi, S. and Motta, E. (2010) Personal Monitoring of Web Information Exchange: Towards Web Lifelogging, Web Science 2010
- 14. From Personal Data to Trust d'Aquin, M., Elahi, S. and Motta, E. (2010) Semantic Monitoring of Personal Web Activity to Support the Management of Trust and Privacy, SPOT @ ESWC 2010
- 15. Transparency through Personal Analytics The UCIAD personal analytics dashboard: Showing their activity data to users. User study to investigate implication on privacy for the user and the organisation. Thomas, K. (2014) Privacy Implications of Online Consumer-Activity Data: An Empirical Study, Privacy Online @ ISWC 2014
- 16. Making it more complex There are some obvious privacy nightmares… for to make sense of.
- 17. Basic FAcebook Ontology (extract)
- 18. Example of Epistemic rules Ka Post(X) :- author(X, a) Ka Post(X) :- scope(X, All_friends), author(X, b), friend(b,a) Ka Post(X) :- friend(a,b), includes(X,b) Ka wasIn(P,Y) :- includes(X,Y), in(X,P), Ka Post(X) Ka wasWith(X,Y) :- includes(P,X), includes(P,Y), Ka Post(X)
- 19. EPIFABO d'Aquin, M. and Thomas, K. (2013) Modeling and Reasoning Upon Facebook Privacy Settings, Demo at International Semantic Web Conference, ISWC
- 20. Ongoing stuff Personal analytics and ontological reasoning for learning analytics (new H2020 project AFEL - Analytics for Everyday Learning) Ontologies and Linked Data to handle the process of curating data, including for terms of use (licences) and data protection. http://dpachecker.kmi.open.ac.uk/ http://data.open.ac.uk/licence-picker/
- 21. MK Data Hub - MK:Smart Triple Store RDBMS NoSQL DB DataCatalogue AuthorisationServer Data Compilation Developper policies policiesdata Locations Stats Events Sensor Social media d'Aquin,M.,Adamou,A.,Daga,E.,Liu,S.,Thomas,K.andMotta,E.(2014) DealingwithDiversityinaSmart-CityDatahub,SemanticsforSmarterCities @ISWC2014
- 22. Triple Store RDBMS NoSQL DB DataCatalogue AuthorisationServer Data Compilation Developper policies policiesdata Rights? Locations Stats Events Sensor Social media d'Aquin,M.,Adamou,A.,Daga,E.,Liu,S.,Thomas,K.andMotta,E.(2014) DealingwithDiversityinaSmart-CityDatahub,SemanticsforSmarterCities @ISWC2014 MK Data Hub - MK:Smart
- 23. MK Data Hub - MK:Smart Locations Stats Events Sensor Social media Triple Store RDBMS NoSQL DB DataCatalogue AuthorisationServer Data Compilation Developper policies policiesdata Rights? Privacy Impact Assessment? d'Aquin,M.,Adamou,A.,Daga,E.,Liu,S.,Thomas,K.andMotta,E.(2014) DealingwithDiversityinaSmart-CityDatahub,SemanticsforSmarterCities @ISWC2014