A Privacy-Preserving Energy Theft Detection Model for Effective Demand-Response Management in Smart Grids. (arXiv:2303.13204v1 [cs.CR])
The detection of energy thefts is vital for the safety of the whole smart grid system. However, the detection alone is not enough since energy thefts can crucially affect the…
Don’t FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs. (arXiv:2303.13211v1 [cs.CR])
In this paper we investigate the frequency sensitivity of Deep Neural Networks (DNNs) when presented with clean samples versus poisoned samples. Our analysis shows significant disparities in frequency sensitivity between…
Don’t Peek at My Chart: Privacy-preserving Visualization for Mobile Devices. (arXiv:2303.13307v1 [cs.HC])
Data visualizations have been widely used on mobile devices like smartphones for various tasks (e.g., visualizing personal health and financial data), making it convenient for people to view such data…
A Privacy-Preserving Energy Theft Detection Model for Effective Demand-Response Management in Smart Grids. (arXiv:2303.13204v1 [cs.CR])
The detection of energy thefts is vital for the safety of the whole smart grid system. However, the detection alone is not enough since energy thefts can crucially affect the…
Adversarial Robustness of Learning-based Static Malware Classifiers. (arXiv:2303.13372v1 [cs.CR])
Malware detection has long been a stage for an ongoing arms race between malware authors and anti-virus systems. Solutions that utilize machine learning (ML) gain traction as the scale of…
Don’t FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs. (arXiv:2303.13211v1 [cs.CR])
In this paper we investigate the frequency sensitivity of Deep Neural Networks (DNNs) when presented with clean samples versus poisoned samples. Our analysis shows significant disparities in frequency sensitivity between…
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. (arXiv:2106.06312v4 [cs.LG] UPDATED)
Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where parties share the same set of samples…
Adversarial Robustness of Learning-based Static Malware Classifiers. (arXiv:2303.13372v1 [cs.CR])
Malware detection has long been a stage for an ongoing arms race between malware authors and anti-virus systems. Solutions that utilize machine learning (ML) gain traction as the scale of…
Don’t FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs. (arXiv:2303.13211v1 [cs.CR])
In this paper we investigate the frequency sensitivity of Deep Neural Networks (DNNs) when presented with clean samples versus poisoned samples. Our analysis shows significant disparities in frequency sensitivity between…
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. (arXiv:2303.13408v1 [cs.CL])
To detect the deployment of large language models for malicious use cases (e.g., fake content creation or academic plagiarism), several approaches have recently been proposed for identifying AI-generated text via…