Search Results for author:
Found 3 papers, 1 papers with code
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
However, despite their promising efficiency, almost all existing machine unlearning methods handle unlearning requests independently from inference requests, which unfortunately introduces a new security issue of inference service obsolescence and a privacy vulnerability of undesirable exposure for machine unlearning in MLaaS.
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
