Search Results for author: Z. Berkay Celik

Found 11 papers, 4 papers with code

New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning

no code implementations28 Jul 2021 Siddharth Divi, Yi-Shan Lin, Habiba Farrukh, Z. Berkay Celik

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.

Fairness Personalized Federated Learning

What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors

no code implementations22 Sep 2020 Yi-Shan Lin, Wen-Chuan Lee, Z. Berkay Celik

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

On the Feasibility of Exploiting Traffic Collision Avoidance System Vulnerabilities

no code implementations25 Jun 2020 Paul M. Berges, Basavesh Ammanaghatta Shivakumar, Timothy Graziano, Ryan Gerdes, Z. Berkay Celik

Traffic Collision Avoidance Systems (TCAS) are safety-critical systems required on most commercial aircrafts in service today.

Real-time Analysis of Privacy-(un)aware IoT Applications

no code implementations24 Nov 2019 Leonardo Babun, Z. Berkay Celik, Patrick McDaniel, A. Selcuk Uluagac

We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users.

KRATOS: Multi-User Multi-Device-Aware Access Control System for the Smart Home

no code implementations22 Nov 2019 Amit Kumar Sikder, Leonardo Babun, Z. Berkay Celik, Abbas Acar, Hidayet Aksu, Patrick McDaniel, Engin Kirda, A. Selcuk Uluagac

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

Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities

1 code implementation18 Sep 2018 Z. Berkay Celik, Earlence Fernandes, Eric Pauley, Gang Tan, Patrick McDaniel

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

Sensitive Information Tracking in Commodity IoT

1 code implementation22 Feb 2018 Z. Berkay Celik, Leonardo Babun, Amit K. Sikder, Hidayet Aksu, Gang Tan, Patrick McDaniel, A. Selcuk Uluagac

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

Patient-Driven Privacy Control through Generalized Distillation

no code implementations26 Nov 2016 Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel

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.

Detection under Privileged Information

no code implementations31 Mar 2016 Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami

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.

Face Recognition Malware Classification +1

Practical Black-Box Attacks against Machine Learning

17 code implementations8 Feb 2016 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami

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

12 code implementations24 Nov 2015 Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami

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.

Adversarial Attack Adversarial Defense

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