21 citations · 46 across the 15 of their papers we have counts for
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cs.GT2017
Together or Alone: The Price of Privacy in Collaborative Learning
Balazs Pejo, Qiang Tang, Gergely Biczok
Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the un…
cs.IR2017
A Probabilistic View of Neighborhood-based Recommendation Methods
Jun Wang, Qiang Tang
Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors…
cs.CR2017
Differentially Private Neighborhood-based Recommender Systems
Jun Wang, Qiang Tang
Privacy issues of recommender systems have become a hot topic for the society as such systems are appearing in every corner of our life. In contrast to the fact that many secure mu…