166 citations · 367 across the 8 of their papers we have counts for
10 papers
MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar, Atul Sharma, Z. Berkay Celik +1
Recent works in federated learning (FL) have shown the utility of leveraging transfer learning for balancing the benefits of FL and centralized learning. In this setting, federated…
KRATOS: Multi-User Multi-Device-Aware Access Control System for the Smart Home
Amit Kumar Sikder, Leonardo Babun, Z. Berkay Celik +5
In a smart home system, multiple users have access to multiple devices, typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consid…
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
Z. Berkay Celik, Earlence Fernandes, Eric Pauley +2
Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive---new app…
Soteria: Automated IoT Safety and Security Analysis
Z. Berkay Celik, Patrick McDaniel, Gang Tan
Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital systems have changed the way we live, play and work.…
Achieving Secure and Differentially Private Computations in Multiparty Settings
Abbas Acar, Z. Berkay Celik, Hidayet Aksu +2
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (S…
Curie: Policy-based Secure Data Exchange
Z. Berkay Celik, Hidayet Aksu, Abbas Acar +3
Data sharing among partners---users, organizations, companies---is crucial for the advancement of data analytics in many domains. Sharing through secure computation and differentia…
Patient-Driven Privacy Control through Generalized Distillation
Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel
The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers,…
Detection under Privileged Information
Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov +4
For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network…
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow +3
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing…
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha +3
Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfe…