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Found 7 papers, 1 papers with code
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer
When receiving the unlearning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method.
Defense Against Multi-target Trojan Attacks
To defend against this attack, we first introduce a trigger reverse-engineering mechanism that uses multiple images to recover a variety of potential triggers.
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Compared with a representative SSBA as a baseline ($SSBA_{Base}$), $CASSOCK$-based attacks have significantly advanced the attack performance, i. e., higher ASR and lower FPR with comparable CDA (clean data accuracy).
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious.
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning
To address the issue of privacy-preserving in collaborative learning, secure outsourced computation and federated learning are two typical methods.
Publicly Verifiable Databases With All Efficient Updating Operations
As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion.
Identifying Malicious Web Domains Using Machine Learning Techniques with Online Credibility and Performance Data
Malicious web domains represent a big threat to web users' privacy and security.
