Lightweight Distributed Trust Propagation

Slide 1: Lightweight Distributed Trust Propagation (daniele quercia & stephen hailes & licia capra)

Slide 2: What I do...

Slide 3: Research @ ...

Slide 5: What I research...

Slide 6: Reputation Systems for Mobiles

Slide 7: Daniele Quercia Situation: People exchange digital content

Slide 8: Daniele Quercia Problem: Content overload

Slide 9: Daniele Quercia Proposal: Accept content only from reputable people

Slide 10: Daniele Quercia Take two devices… A B

Slide 11: Daniele Quercia Shall I accept content from B? A B

Slide 12: Daniele Quercia A should set its initial trust for B A B

Slide 13: Daniele Quercia A should set its initial trust for B A B Yes, but how?

Slide 14: Daniele Quercia Traditional way: C Trust propagation ? A B

Slide 15: Daniele Quercia  That way works on the Web and has been tested on “binary” ratings

Slide 16: Daniele Quercia If that way is tested on 3-level ratings

Slide 17: Conclusion: A needs a new way for propagating its trust in B

Slide 18: Conclusion: A needs a new way for propagating its trust in B But which way?

Slide 19: Our proposal

Slide 20: Daniele Quercia AB C 1 2 ? AC CB A B

Slide 21: Daniele Quercia Put trust relationships in a new graph & label graph nodes ? AB C 1 2 ? AC CB A B 1 2

Slide 22: Daniele Quercia Define a Put trust relationships “good” function in a new graph & that rates AB label graph nodes ? AB C f 1 2 ? AC CB A B 1 2

Slide 23: Daniele Quercia find_f build_graph(trust_relationships, AB) ? AB C f 1 2 ? AC CB A B 1 2

Slide 24: <build_graph>

Slide 25: ? A B 1 AB 1 0.5 1 2 3 ? ? 2 D C M M 1 2 3

Slide 26: ? A B 1 AB 1 0.5 1 2 3 ? ? 2 D C M M 1 2 3 What are those ?

Slide 27: Remember, we’ll define f We will see that f rates neighbouring nodes alike It’s a neighbour of AB; ? therefore, it rates as AB does

Slide 28: Remember, we’ll define f We will see that f rates neighbouring nodes alike It’s a neighbour of AB; ? therefore, it rates as AB does Find relationships that rate as AB does

Slide 29: Remember, we’ll define f We will see that f rates neighbouring nodes alike It’s a neighbour of AB; ? therefore, it rates as AB does

Slide 30: 2 Types of those relationships

Slide 31: 1. The relationships with same rater (A) A B 1 2 3 2 D C 1 As long as B and D behave alike

Slide 32: 2. The relationships with same rated node (B) A B 1 2 3 2 D C 1 As long as A and C rate alike

Slide 33: OK, 2 Types

Slide 34: Taking those relationships, one comes up with …

Slide 35: …a graph! AC 1 AB 0.5 1 AD CB M M 2 3

Slide 36: </build_graph>

Slide 37: <find_f>

Slide 38: AC 1 AB 0.5 1 AD CB M M 2 3

Slide 39: AC 1 AB 0.5 1 AD CB M M 2 3 Take f that returns the ratings already there ( 2 3 ) & similar ratings for neighbouring nodes

Slide 40: More formally, …

Slide 41: We find f that minimizes that loss!

Slide 42: </find_f>

Slide 43: Daniele Quercia find_f build_graph ? AB C f 1 2 ? AC CB A B 1 2

Slide 44: Does it work? Useful, Robust, Fast, “Light”?

Slide 45: Daniele Quercia Useful? Tested on real data (Advogato: > 55K user ratings)

Slide 46: Daniele Quercia Useful? Tested on real data (Advogato: > 55K user ratings)

Slide 47: Daniele Quercia Robust?

Slide 48: Daniele Quercia Robust?

Slide 49: Daniele Quercia Fast and “Light”?

Slide 50: Daniele Quercia Fast and “Light”? For propagating AB (worst case) 30KB Transmit & run for 2.8ms

Slide 51: Daniele Quercia All this on …

Slide 52: Daniele Quercia All this on …

Slide 53: Daniele Quercia