Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- surveyOctober 2024
A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
ACM Computing Surveys (CSUR), Volume 57, Issue 1Article No.: 2, Pages 1–38https://doi.org/10.1145/3679014In recent years, the notion of “the right to be forgotten” (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their ...
- research-articleJuly 2024
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer
SecTL '24: Proceedings of the 2nd ACM Workshop on Secure and Trustworthy Deep Learning SystemsPages 47–55https://doi.org/10.1145/3665451.3665532In light of the widespread application of Automatic Speech Recognition (ASR) systems, their security concerns have received much more attention than ever before, primarily due to the susceptibility of Deep Neural Networks. Previous studies have ...
- short-paperMay 2024
Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 943–946https://doi.org/10.1145/3589335.3651540In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-level targets with intact structures. Focus on these challenges, this paper ...
- short-paperMay 2024
LinkGuard: Link Locally Privacy-Preserving Graph Neural Networks with Integrated Denoising and Private Learning
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 593–596https://doi.org/10.1145/3589335.3651533Recent studies have introduced privacy-preserving graph neural networks to safeguard the privacy of sensitive link information in graphs. However, existing link protection mechanisms in GNNs, particularly over decentralized nodes, struggle to strike an ...
- short-paperMay 2024
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 633–636https://doi.org/10.1145/3589335.3651460Monocular 3D face reconstruction plays a crucial role in avatar generation, with significant demand in web-related applications such as generating virtual financial advisors in FinTech. Current reconstruction methods predominantly rely on deep learning ...
- research-articleJuly 2023
A Practical Intrusion Detection System Trained on Ambiguously Labeled Data for Enhancing IIoT Security
CPSS '23: Proceedings of the 9th ACM Cyber-Physical System Security WorkshopPages 14–23https://doi.org/10.1145/3592538.3594270As a special class of the Internet-of-Things (IoT), Industrial Internet-of-Things (IIoT) enhance the efficiency of manufacturing and industrial processes by utilizing smart components and new technologies in industrial sectors. With the increasing ...
- research-articleJuly 2023
Privacy-Enhanced Knowledge Transfer with Collaborative Split Learning over Teacher Ensembles
SecTL '23: Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems WorkshopArticle No.: 1, Pages 1–13https://doi.org/10.1145/3591197.3591303Knowledge Transfer has received much attention for its ability to transfer knowledge, rather than data, from one application task to another. In order to comply with the stringent data privacy regulations, privacy-preserving knowledge transfer is highly ...
- research-articleJanuary 2023
Long-Term Privacy-Preserving Aggregation With User-Dynamics for Federated Learning
IEEE Transactions on Information Forensics and Security (TIFS), Volume 18Pages 2398–2412https://doi.org/10.1109/TIFS.2023.3266919Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced federated learning (FL), which enables the server to obtain the sum of users’ locally trained models while keeping local training data private. However, most ...
- research-articleJanuary 2023
Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning
IEEE Transactions on Information Forensics and Security (TIFS), Volume 18Pages 1839–1854https://doi.org/10.1109/TIFS.2022.3163592Machine learning (ML) has been widely recognized as an enabler of the global trend of digital transformation. With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could ...