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1. Federated Learning on Distributed and Encrypted Data for Smart Manufacturing

   https://doi.org/10.1115/1.4065571  

   Kuo, Timothy; Yang, Hui (July 2024, Journal of Computing and Information Science in Engineering)

   Abstract Industry 4.0 drives exponential growth in the amount of operational data collected in factories. These data are commonly distributed and stored in different business units or cooperative companies. Such data-rich environments increase the likelihood of cyber attacks, privacy breaches, and security violations. Also, this poses significant challenges on analytical computing on sensitive data that are distributed among different business units. To fill this gap, this article presents a novel privacy-preserving framework to enable federated learning on siloed and encrypted data for smart manufacturing. Specifically, we leverage fully homomorphic encryption (FHE) to allow for computation on ciphertexts and generate encrypted results that, when decrypted, match the results of mathematical operations performed on the plaintexts. Multilayer encryption and privacy protection reduce the likelihood of data breaches while maintaining the prediction performance of analytical models. Experimental results in real-world case studies show that the proposed framework yields superior performance to reduce the risk of cyber attacks and harness siloed data for smart manufacturing. 

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2. Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus

   https://doi.org/10.1145/3485730.3493684  

   Nagothu, Deeraj; Xu, Ronghua; Chen, Yu; Blasch, Erik; Aved, Alexander (November 2021, The 19th ACM Conference on Embedded Networked Sensor Systems)

   Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment. 

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3. Distributed Logic Encryption: Essential Security Requirements and Low-Overhead Implementation

   https://doi.org/10.1145/3526241.3530372  

   Afsharmazayejani, Raheel; Sayadi, Hossein; Rezaei, Amin (June 2022, In Proceedings of Great Lakes Symposium on VLSI (GLSVLSI))

   Due to outsource manufacturing, the semiconductor industry must deal with various hardware threats such as piracy and overproduction. To prevent illegal electronic products from functioning, the circuit can be encrypted using a protected key only known to the designer. However, an attacker can still decipher the secret key utilizing a functioning circuit bought from the market, and the encrypted layout leaked from an untrusted foundry. In this paper, after introducing essential conformity and mutuality features for secure logic encryption, we propose DLE, a novel Distributed Logic Encryption design that resists against all known oracle guided and structural attacks including the newly proposed fault-aided SAT-based attack that iteratively injects a single stuck-at fault to thwart the locking effect. DLE forces the attacker to insert multiple stuck-at faults simultaneously in critical points to achieve a smaller but meaningful encrypted circuit; thus, exponentially reducing the chance to hit all the critical points with properly located stuck-at fault injections. Our experiments confirm that DLE maintains an exponentially high degree of security under diverse attacks with the polynomial area and linear performance overheads. 

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4. Stochastic ADMM Based Distributed Machine Learning with Differential Privacy

   https://doi.org/10.1007/978-3-030-37228-6_13  

   Ding, Jiahao; Errapotu, Sai Mounika; Zhang, Haijun; Gong, Yanmin; Pan, Miao; Han, Zhu (December 2019, Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering)

   While embracing various machine learning techniques to make effective decisions in the big data era, preserving the privacy of sensitive data poses significant challenges. In this paper, we develop a privacy-preserving distributed machine learning algorithm to address this issue. Given the assumption that each data provider owns a dataset with different sample size, our goal is to learn a common classifier over the union of all the local datasets in a distributed way without leaking any sensitive information of the data samples. Such an algorithm needs to jointly consider efficient distributed learning and effective privacy preservation. In the proposed algorithm, we extend stochastic alternating direction method of multipliers (ADMM) in a distributed setting to do distributed learning. For preserving privacy during the iterative process, we combine differential privacy and stochastic ADMM together. In particular, we propose a novel stochastic ADMM based privacy-preserving distributed machine learning (PS-ADMM) algorithm by perturbing the updating gradients, that provide differential privacy guarantee and have a low computational cost. We theoretically demonstrate the convergence rate and utility bound of our proposed PS-ADMM under strongly convex objective. Through our experiments performed on real-world datasets, we show that PS-ADMM outperforms other differentially private ADMM algorithms under the same differential privacy guarantee. 

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5. Threats on Logic Locking: A Decade Later

   https://doi.org/10.1145/3299874.3319495  

   Zamiri Azar, Kimia; Mardani Kamali, Hadi; Homayoun, Houman; Sasan, Avesta (May 2019, 2019 ACM Great Lakes Symposium on VLSI, GLSVLSI 2019)

   To reduce the cost of ICs and to meet the market's demand, a considerable portion of manufacturing supply chain, including silicon fabrication, packaging and testing may be pushed offshore. Utilizing a global IC manufacturing supply chain, and inclusion of non-trusted parties in the supply chain has raised concerns over security and trust related challenges including those of overproduction, counterfeiting, IP piracy, and Hardware Trojans to name a few. To reduce the risk of IC manufacturing in an untrusted and globally distributed supply chain, the researchers have proposed various locking and obfuscation mechanisms for hiding the functionality of the ICs during the manufacturing, that requires the activation of the IP after fabrication using the key value(s) that is only known to the IP/IC owner. At the same time, many such proposed obfuscation and locking mechanisms are broken with attacks that exploit the inherent vulnerabilities in such solutions. The past decade of research in this area, has resulted in many such defense and attack solutions. In this paper, we review a decade of research on hardware obfuscation from an attacker perspective, elaborate on attack and defense lessons learned, and discuss future directions that could be exploited for building stronger defenses. 

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