activity
20182021
most citedDifferential Privacy for Eye-Tracking Data

63 citations · 64 across the 4 of their papers we have counts for

collaborators

6 papers

cs.GT20211 cited

Truthful Information Elicitation from Hybrid Crowds

Qishen Han, Sikai Ruan, Yuqing Kong +3

Suppose a decision maker wants to predict weather tomorrow by eliciting and aggregating information from crowd. How can the decision maker incentivize the crowds to report their in…

cs.LG2021

Certifiably Robust Interpretation via Renyi Differential Privacy

Ao Liu, Xiaoyu Chen, Sijia Liu +2

Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of…

cs.CY2021

The Smoothed Likelihood of Doctrinal Paradox

Ao Liu, Lirong Xia

When aggregating logically interconnected judgments from agents, the result might be inconsistent with the logical connection. This inconsistency is known as the doctrinal para…

cs.CR201963 cited

Differential Privacy for Eye-Tracking Data

Ao Liu, Lirong Xia, Andrew Duchowski +3

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datas…

cs.LG2018

Towards Non-Parametric Learning to Rank

Ao Liu, Qiong Wu, Zhenming Liu +1

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with…

cs.CR2018

How Private Are Commonly-Used Voting Rules?

Ao Liu, Yun Lu, Lirong Xia +1

Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting sc…