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Adding Privacy to Netflix Recommendations Frank McSherry, Ilya Mironov (MSR SVC). Attacks on Recommender Systems — No “blending in”, auxiliary information — Differencing attacks/active attacks — Potential threats: — re-identification, linking of profiles — business, legal liabilities. - PowerPoint PPT Presentation
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Adding Privacy to Netflix RecommendationsFrank McSherry, Ilya Mironov (MSR SVC)
Attacks on Recommender Systems
— No “blending in”, auxiliary information— Differencing attacks/active attacks— Potential threats:
— re-identification, linking of profiles— business, legal liabilities
“Users like you” “Enjoyed by members who enjoyed”
C
CB A A B C
D E F :
ADE
?
?
Differential Privacy
Strong formal privacy definition. Informally:“Any output of the computation is as likely with your data as without.”
Privacy for a Count: How Many Ratings?
Current Architectures:
DP
Private Architecture:
Any output is as likely with your data as without.
Netflix Prize Dataset
17K movies480K people100M ratings3M unknowns
$1M for beating the benchmark by 10%
0.032 0.32 3.20.8800000000000010.9000000000000010.9200000000000010.9400000000000010.9600000000000010.980000000000001
11.02
Cinematchglobal effectskNNSVD
1/σ – privacy parameter
RMSE
benchmark
Differentially Private Recommendation
1.Global effects (movie/user averages)2.Movie-movie covariance matrix
3.Leading “geometric” Netflix algorithms
Accuracy-Privacy Tradeoff
DP
Cost of Privacy over Time
0 500 1000 1500 20000%
4%
8%
12%
16%
0
20000000
40000000
60000000
80000000
100000000
lossrecords
days