Name
Abstract | ||
|---|---|---|
Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain sensitive personal information (e.g., political views). This potentially even inhibits the user from providing information in the first place. Current distributed privacy-preserving association rules mining (PPARM) approaches use cryptographic primitives that come with high computational and communication costs, rendering PPARM unsuitable for large-scale applications such as social networks. We propose improvements in the efficiency and privacy of PPARM approaches by minimizing the required data. We propose and compare sampling strategies to sample the data based on social graphs in a privacy-preserving manner. The results on real-world datasets show that our sampling-based approach can achieve a high average precision score with as low as 50% sampling rate and, therefore, with a 50% reduction of communication cost.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3347013 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
association rules, distributed data, efficiency, privacy-preserving data mining, recommender system | Recommender system,Graph,Computer science,Association rule learning,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 3 | 0.38 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aidmar Wainakh | 1 | 7 | 3.13 |
| Tim Grube | 2 | 19 | 7.54 |
| Jörg Daubert | 3 | 43 | 8.79 |
| Max Mühlhäuser | 4 | 1652 | 252.87 |