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distributed_ml [2018/03/14 22:18] patra |
distributed_ml [2018/04/05 19:01] damaskin |
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[2] __[[http://www.vldb.org/pvldb/vol9/p1695-upadhyaya.pdf|Price-Optimal Querying with Data APIs]]__\\ | [2] __[[http://www.vldb.org/pvldb/vol9/p1695-upadhyaya.pdf|Price-Optimal Querying with Data APIs]]__\\ | ||
[3] __[[http://pages.cs.wisc.edu/~paris/papers/data_pricing.pdf|Query-Based Data Pricing]]__\\ | [3] __[[http://pages.cs.wisc.edu/~paris/papers/data_pricing.pdf|Query-Based Data Pricing]]__\\ | ||
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+ | ===Black-Box Attacks against Recommender Systems=== | ||
+ | A recommender system can be viewed as a black-box that users query with feedback (e.g., ratings, clicks) before getting the output list of recommendations. | ||
+ | The goal is to infer properties of the recommendation algorithm by observing the output from different queries. | ||
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+ | Related papers:\\ | ||
+ | [1] __[[https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_tramer.pdf|Stealing Machine Learning Models via Prediction APIs]]__\\ | ||
+ | [2] __[[https://arxiv.org/pdf/1602.02697v3.pdf|Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples]]__\\ | ||
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**Contact:** __[[http://people.epfl.ch/georgios.damaskinos|Georgios Damaskinos]]__ | **Contact:** __[[http://people.epfl.ch/georgios.damaskinos|Georgios Damaskinos]]__ | ||