The presentation discusses the importance of provenance in establishing privacy-proof trust, highlighting issues such as the cold-start problem, privacy intrusion, and inaccurate predictions. It emphasizes using provenance stereotypes to enhance trust estimation by aggregating user behavior patterns while maintaining privacy. The conclusion suggests standardizing the creation of stereotypes to balance performance, evidence availability, and privacy considerations.

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Provenance as a Key Factor for
Privacy-proof Trust
Davide Ceolin

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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

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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

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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

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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

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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

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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

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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

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Thanks
d.ceolin@vu.nl