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Found 13 papers, 6 papers with code
Practical Black-Box Attacks against Machine Learning
Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN.
The Limitations of Deep Learning in Adversarial Settings
In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.
Rethinking How to Evaluate Language Model Jailbreak
We evaluate our metrics on a benchmark dataset produced from three malicious intent datasets and three jailbreak systems.
Sensitive Information Tracking in Commodity IoT
Through this effort, we introduce a rigorously grounded framework for evaluating the use of sensitive information in IoT apps---and therein provide developers, markets, and consumers a means of identifying potential threats to security and privacy.
Cryptography and Security Programming Languages
Detection under Privileged Information
In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems.
Patient-Driven Privacy Control through Generalized Distillation
In this paper, we present privacy distillation, a mechanism which allows patients to control the type and amount of information they wish to disclose to the healthcare providers for use in statistical models.
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
Based on a study of five IoT programming platforms, we identify the key insights resulting from works in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues.
Cryptography and Security Programming Languages
Real-time Analysis of Privacy-(un)aware IoT Applications
We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users.
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors
EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network predicts inputs through model saliency explanations that highlight the parts of the inputs deemed important to arrive a decision at a specific target.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
KRATOS: Multi-User Multi-Device-Aware Access Control System for the Smart Home
Users can specify their desired access control settings using the interaction module which are translated into access control policies in the backend server.
Cryptography and Security
On the Feasibility of Exploiting Traffic Collision Avoidance System Vulnerabilities
Traffic Collision Avoidance Systems (TCAS) are safety-critical systems required on most commercial aircrafts in service today.
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning
In this setting, the non-IID distribution of the data across clients restricts the global FL model from delivering good performance on the local data of each client.
Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions
In this paper, we measure their ability to refute popular S&P misconceptions that the general public holds.
