provenance as a key factor for privacy-proof trust

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Page 1: Provenance as a Key Factor for Privacy-proof Trust

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Provenance as a Key Factor for Privacy-proof Trust

Davide Ceolin

Page 2: Provenance as a Key Factor for Privacy-proof Trust

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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

Page 3: Provenance as a Key Factor for Privacy-proof Trust

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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

Page 4: Provenance as a Key Factor for Privacy-proof Trust

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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

Page 5: Provenance as a Key Factor for Privacy-proof Trust

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Morbi leo risus, porta ac consectetur ac, vestibulum at eros.

This is the presentation title www.amsterdamdatascience.nl

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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

Page 6: Provenance as a Key Factor for Privacy-proof Trust

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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

Page 7: Provenance as a Key Factor for Privacy-proof Trust

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This is the presentation title www.amsterdamdatascience.nl

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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

Page 8: Provenance as a Key Factor for Privacy-proof Trust

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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

Page 9: Provenance as a Key Factor for Privacy-proof Trust

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