Sapienza trust model derivation showing equivalence with random answers
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So far we have stated that the trust model in Sapienza ( https://ceur-ws.org/Vol-1664/w9.pdf) is the same as modeling the untrustworthy part of a source as random. What does this mean? If I trust my source 90%, then 10% of its reporting is untrustworthy, meaning it answers the question randomly for that 10%.
Let's take a look at some examples and then try to derive a general equation which we will then equate to the trust equation in Sapienza.
We will use the real/fake node case because it is simple to follow. We have 100 new nodes and a source that reports with 60% confidence that the nodes are real (or fake). For purposes of review we'll take the case of 100% trust first. For a single source we would now have a confidence of 60% that the node is real. We just believe the source and no calculation is required.
For two sources at 60% we can model the situation as follows:
100 N
60% 60%
50 R ==> 30 Tr ==> 18 Tr (ie, for 50 Real nodes, our first 60% confident source reports 30 to be real (Tr) and 20 to be fake (Tf). Our second
source independently does the same.
12 Tf
20 Tf ==> 12 Tr
8 Tf
50 F ==> 30 Tf ==> 18 Tf (same as above for 50 Fake nodes. It is important to note that if our source is 60% confident about real nodes it is also
60% confident about fake nodes)
12 Tr
20 Tr ==> 12 Tf
8 Tr
This is two 60% tests in a row. If two tests in a row say it's real, what is the probability of it being real? Ie how confident should we now be?
18+8 nodes tested real twice. Of those 18 are actually real ==> 18/(18+8) = 0.692. Now we are 69.2% sure the node is real. This is the same, btw, as the Bayes eqn in Sapienza: 0.6*0.6/(0.6*0.6+0.4*0.4) = 0.692.
Now let's build the trust factor into the eqn. Let's suppose I trust Source 1 90%. This means 10% of the time my source will report randomly that the node is real or fake no matter what their test says it is.
SOURCE 1 WITH TRUST:
100 Nodes With Trust
S1 60% confidence 90% trust (90% of reported answers are same as test and 10% are random)
Totals
50 R ==> 30 Tr (27 rTr, 1.5 rTr, 1.5 rTf) ==> 29.5 rTr
20 Tf (18 rTf, 1 rTr, 1 rTf) 20.5 rTf
50 F ==> 30 Tf (27 rTf, 1.5 rTf, 1.5 rTr) ==> 29.5 rTf
20 Tr (18 rTr, 1 rTr, 1 rTf) 20.5 rTr