Quantifying Voter-controlled Privacy Hugo Jonker in collaboration with Jun Pang and Sjouke Mauw
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Quantifying Voter-controlled Privacy Hugo Jonker in collaboration with Jun Pang and Sjouke Mauw
description
Quantifying Voter-controlled Privacy Hugo Jonker in collaboration with Jun Pang and Sjouke Mauw. Why care about privacy?. A random set of voters. Traditional view on privacy. ?. ?. ?. Works for: vote-privacy, receipt-freeness, coercion-resistance. Privacy ≠ swapping. - PowerPoint PPT Presentation
Transcript of Quantifying Voter-controlled Privacy Hugo Jonker in collaboration with Jun Pang and Sjouke Mauw
Quantifying
Voter-controlled Privacy
Hugo Jonker
in collaboration with Jun Pang and Sjouke Mauw
Why care about privacy?
A random set of voters
Traditional view on privacy
Works for: vote-privacy, receipt-freeness, coercion-resistance
? ? ?
Privacy ≠ swapping
What could that have been?
- ρ( ) =
- ρ(
- ρ({
) = (ρ( ), ρ( )) ,
}K) = {ρ( )}K if k is known
- ρ({ }K) = if k is unknown01101010101011010011101010110011010110101100101010110111011010101
Choice groups
) =
cg(
}{
Conspiring voters
+ =
+ =
...
How to conspire
{ }kuntappable channels
sees all!
=01101010101011010011101010110011010110101100101010110111011010101
+ k = { }k
Untappable channels? ? ?
Untappable channels
01101010101011010011101010110011010110101100101010110111011010101
01101010101011010011101010110011010110101100101010110111011010101
Effects of coercion
) = cg(
}{–
) =
cg( }{– =?
Plans
• slight extensions to formalism• account for distribution in result• conspiring authorities, defense coalitions• extend to auctions, e-healthcare, …
• Sec ote 2010
Thanks for your attention
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