ANDRUSPEX: Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices

In Proceedings of the 2021 IEEE European Symposium on Security and Privacy (EUROS&P 2021)

How Did That Get In My Phone? Unwanted App Distribution on Android Devices

In Proceedings of the 42st IEEE Symposium on Security and Privacy (S&P 2021)

Understanding Worldwide Private Information Collection on Android

In Proceedings of the 2021 Network and Distributed System Security Symposium (NDSS 2021)

Journey to the Center of the Cookie Ecosystem: Unraveling Actors' Roles and Relationships

In Proceedings of the 42nd IEEE Symposium on Security and Privacy (S&P 2021) Our analysis lets us paint a highly detailed picture of the cookie ecosystem, discovering an intricate network of connections between players that reciprocally exchange information and include each other's content in web pages whose owners may not even be aware.

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Secure Systems

Central to trust in an increasingly digital world is the ability to detect and prevent attacks in modern (and not so modern) information systems. This research includes building secure software, supporting forensics, malware analysis, browser/web/network security, and information-centric security.

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Privacy, Identity, and Trust

Consumers and corporations are driven to engage in a digital world that they cannot adequately trust. We are developing paradigms to enable online commerce and facilitate machine learning in ways that provide privacy and protect user identities, by leveraging such concepts as local differential privacy, federated machine learning, identity brokering, and blockchain technology.

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Robust and Fair Machine Learning, Data Mining, and Artificial Intelligence

The tremendous growth in the learning capacity of Machine Learning methods has yet to be met with a corresponding growth in our ability to understand these models. Equally troubling, our ability to build robust machine learning models has not kept pace with research in adversarial attacks against machine learning. As we increasingly hand over decision making to automated machine learning and AI systems, we must find ways that the life-altering decisions made by these systems can be audited for fairness, safety, robustness to adversaries, and the preservation of privacy of any personally identifiable information over which they operate.

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