Name
Abstract | ||
|---|---|---|
Recommender systems typically require users to reveal their ratings to a recommender service, which subsequently uses them to provide relevant recommendations. Revealing ratings has been shown to make users susceptible to a broad set of inference attacks, allowing the recommender to learn private user attributes, such as gender, age, etc. In this work, we show that a recommender can profile items without ever learning the ratings users provide, or even which items they have rated. We show this by designing a system that performs matrix factorization, a popular method used in a variety of modern recommendation systems, through a cryptographic technique known as garbled circuits. Our design uses oblivious sorting networks in a novel way to leverage sparsity in the data. This yields an efficient implementation, whose running time is O(Mlog^2M) in the number of ratings M. Crucially, our design is also highly parallelizable, giving a linear speedup with the number of available processors. We further fully implement our system, and demonstrate that even on commodity hardware with 16 cores, our privacy-preserving implementation can factorize a matrix with 10K ratings within a few hours. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2508859.2516751 | ACM Conference on Computer and Communications Security |
Keywords | Field | DocType |
recommender system,modern recommendation system,efficient implementation,matrix factorization,ratings user,available processor,privacy-preserving implementation,recommender service,revealing rating,ratings m. crucially,recommender systems,privacy | Recommender system,Sorting network,Computer security,Cryptography,Computer science,Inference,Matrix (mathematics),Matrix decomposition,Theoretical computer science,Factorization,Speedup | Conference |
Citations | PageRank | References |
79 | 1.90 | 48 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valeria Nikolaenko | 1 | 332 | 10.45 |
| Stratis Ioannidis | 2 | 715 | 51.97 |
| Udi Weinsberg | 3 | 454 | 22.51 |
| Marc Joye | 4 | 2413 | 170.74 |
| Nina Taft | 5 | 2109 | 154.92 |
| Dan Boneh | 6 | 21254 | 1398.98 |