Provenance as a Key Factor for Privacy-proof Trust
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Presentation at the "Weaving Relations of Trust in Crowd Work: Transparency and Reputation across Platforms" workshop @ WebSci16
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Provenance as a Key Factor for Privacy-proof Trust
- 1. This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partnerspartners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners Provenance as a Key Factor for Privacy-proof Trust Davide Ceolin
- 2. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • Cold-start Problem. We need observations to build user reputations. • Privacy Intrusion. Knowledge about individuals reduces uncertainty. • Inaccurate Point-wise Prediction. Reputations are asymptotically correct. Open Issues
- 3. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • Reputation systems are (mostly) user-centric. • Besides the who we can use also the when, where and how provenance. Provenance for Trust Estimation waisda.nl
- 4. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • Provenance traces are fine-grained representations of how data came to be. • To derive trust estimations we need to identify links and regularities. • We proposed to use provenance stereotypes: • Clusters of provenance traces representing user behaviours (e.g., early-morning weekend contributors). Provenance for Trust Estimation
- 5. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • By aggregating traces, we can increase the availability of reputations: • Users might be unknown, but their behaviour could be well-known. • This helps mitigating uncertainty. Provenance vs. cold-start
- 6. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • Users tend to adopt uniform behaviours. • We can focus on the provenance stereotype (and hence on the cluster of users) rather than on individuals. • This adds an obfuscation layer. Provenance vs. privacy intrusion
- 7. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • Probabilistic reputations are asymptotically accurate. • Combining individual reputations with stereotypes: • improves accuracy; • allows discriminating among user contributions. Provenance for point-wise predictions
- 8. Slide titel - PT Sans - 28pt Maecenas faucibus mollis interdum. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Morbi leo risus, porta ac consectetur ac, vestibulum at eros. This is the presentation title www.amsterdamdatascience.nl This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners www.amsterdamdatascience.nlProvenance as a Key Factor for Privacy-proof Trust • We adopted provenance stereotypes in a few Cultural Heritage case studies. • The creation of stereotypes needs to be standardised in order to balance: • performance (accuracy and efficiency); • evidence availability; • privacy. Conclusion
- 9. This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partnerspartners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners This is the presentation title this subline can be used for authors partners Thanks d.ceolin@vu.nl