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Works148 from public data
- 685
- Adversarial Attacks and Defenses in Deep Learning676
The theoretical foundations, algorithms, and applications of adversarial attack techniques are introduced and a few research efforts on the defense techniques are described, which cover the broad frontier in the field.
- Feature Importance-aware Transferable Adversarial Attacks353
The Feature Importance-aware Attack (FIA) is proposed, which disrupts important object-aware features that dominate model decisions consistently and achieves stronger transferability.
- Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy350
The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset.
- Generating Synthetic Decentralized Social Graphs with Local Differential Privacy277
This paper proposes LDPGen, a novel multi-phase technique that incrementally clusters users based on their connections to different partitions of the whole population, and derives optimal parameters in this process to cluster structurally-similar users together.
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- Real-time and Spatio-temporal Crowd-sourced Social Network Data Publishing with Differential Privacy197
- Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems169
This paper proposes a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches.
- Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer151
A novel deep learning based system that learns to recognize and reconstruct speech information from the spectrogram representation of acceleration signals and employs adaptive optimization on deep neural networks with skip connections using robust and generalizable losses to achieve robust recognition and reconstruction performance.
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- Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy122
A novel decentralized differential privacy scheme is formulated, which requires that each participant consider not only her own privacy, but also that of her neighbors involved in her ELV, and designs a novel multi-phase framework under DDP that enables an analyst to accurately estimate subgraph counts, an important property of social graphs.
- Towards Efficient Privacy-preserving Image Feature Extraction in Cloud Computing121
This work studies the privacy requirements in outsourcing SIFT computation and proposes SecSIFT, a high performance privacy-preserving SIFT feature detection system that performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.
- Threats to Training: A Survey of Poisoning Attacks and Defenses on Machine Learning Systems120
This survey summarize and categorize existing attack methods and corresponding defenses, as well as demonstrate compelling application scenarios, thus providing a unified framework to analyze poisoning attacks and lay the foundation for a more standardized approach to reproducible studies.
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- e-PoS: Making Proof-of-Stake Decentralized and Fair102
This work proposes a modular version of PoS-based blockchain systems called e-PoS that resists the centralization of network resources by extending mining opportunities to a wider set of stakeholders and leverages the in-built system operations to promote fair mining practices by penalizing malicious entities.
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- FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model85
FedTracker is presented, the first FL model protection framework that provides both ownership verification and traceability and is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks.
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- ShapleyFL: Robust Federated Learning Based on Shapley Value80
This paper proposes a Shapley value-inspired adaptive weighting mechanism, which regards the FL training as sequential cooperative games and adjusts clients' weights according to their contributions, and develops a client sampling strategy based on importance sampling, which can reduce the communication cost by optimizing the variance of the global updates according to the weights of clients.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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