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20162022
most citedPartially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding

7 citations · 23 across the 8 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.LG2020

On Success and Simplicity: A Second Look at Transferable Targeted Attacks

Zhengyu Zhao, Zhuoran Liu, Martha Larson

Achieving transferability of targeted attacks is reputed to be remarkably difficult. Currently, state-of-the-art approaches are resource-intensive because they necessitate training…

cs.CR20205 cited

Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channel

Zhuoran Liu, Niels Samwel, Léo Weissbart +4

We introduce screen gleaning, a TEMPEST attack in which the screen of a mobile device is read without a visual line of sight, revealing sensitive information displayed on the phone…

cs.IR20207 cited

Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding

Manel Slokom, Martha Larson, Alan Hanjalic

This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation…

cs.IR2020

Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start

Zhuoran Liu, Martha Larson

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to…

cs.CV2020

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

Zhengyu Zhao, Zhuoran Liu, Martha Larson

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversar…