Mauro Conti

A smart health application and its related privacy issues

2016

Together with the development of technologies such as those for ubiquitous computing, data mining, Internet of Things (IoT) and wireless sensor networks (WSNs), the concepts of smart cities and mobile health (m-Health) have emerged. Along …

Don't Skype & Type! Acoustic Eavesdropping in Voice-Over-IP

2016 · arXiv (Cornell University)

Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, physical properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models …

Improving password guessing via representation learning

2021 · Padua Research Archive (University of Padova)

Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations.In …

Blockchain-Enabled Secure Energy Trading With Verifiable Fairness in Industrial Internet of Things

2020 · IEEE Transactions on Industrial Informatics

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to …

Looking Through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic

2021

Nowadays living environments are characterized by networks of inter-connected sensing devices that accomplish different tasks, e.g., video surveillance of an environment by a network of CCTV cameras. A malicious user could gather sensitive details on …

Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization

2022 · arXiv (Cornell University)

In the past decades, the rise of artificial intelligence has given us the capabilities to solve the most challenging problems in our day-to-day lives, such as cancer prediction and autonomous navigation. However, these applications might …

Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning

2024 · arXiv (Cornell University)

Source Inference Attack (SIA) in Federated Learning (FL) aims to identify which client used a target data point for local model training. It allows the central server to audit clients' data usage. In cross-silo FL, …