Adding Privacy to Netflix Recommendations Frank McSherry, Ilya Mironov (MSR SVC)
description
Transcript of Adding Privacy to Netflix Recommendations Frank McSherry, Ilya Mironov (MSR SVC)
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