activity
20192021
most citedBoMaNet: Boolean Masking of an Entire Neural Network

6 citations · 8 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2021

Guarding Machine Learning Hardware Against Physical Side-Channel Attacks

Anuj Dubey, Rosario Cammarota, Vikram Suresh +1

Machine learning (ML) models can be trade secrets due to their development cost. Hence, they need protection against malicious forms of reverse engineering (e.g., in IP piracy). Wi…

cs.CR20206 cited

BoMaNet: Boolean Masking of an Entire Neural Network

Anuj Dubey, Rosario Cammarota, Aydin Aysu

Recent work on stealing machine learning (ML) models from inference engines with physical side-channel attacks warrant an urgent need for effective side-channel defenses. This work…

cs.CR2020

Efficacy of Satisfiability Based Attacks in the Presence of Circuit Reverse Engineering Errors

Qinhan Tan, Seetal Potluri, Aydin Aysu

Intellectual Property (IP) theft is a serious concern for the integrated circuit (IC) industry. To address this concern, logic locking countermeasure transforms a logic circuit to…

cs.CR2020

SeqL: Secure Scan-Locking for IP Protection

Seetal Potluri, Aydin Aysu, Akash Kumar

Existing logic-locking attacks are known to successfully decrypt functionally correct key of a locked combinational circuit. It is possible to extend these attacks to real-world Si…

cs.CR20192 cited

MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection

Anuj Dubey, Rosario Cammarota, Aydin Aysu

Differential Power Analysis (DPA) has been an active area of research for the past two decades to study the attacks for extracting secret information from cryptographic implementat…