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1. WaLo: Security Primitive Generator for RT-Level Logic Locking and Watermarking

   https://doi.org/10.1109/AsianHOST51057.2020.9358262  

   Kuai, Jun; He, Jiaji; Ma, Haocheng; Zhao, Yiqiang; Hou, Yumin; Jin, Yier (December 2020, Asian Hardware Oriented Security and Trust Symposium (AsianHOST))

   null (Ed.)

   Various hardware security solutions have been developed recently to help counter hardware level attacks such as hardware Trojan, integrated circuit (IC) counterfeiting and intellectual property (IP) clone/piracy. However, existing solutions often provide specific types of protections. While these solutions achieve great success in preventing even advanced hardware attacks, the compatibility of among these hardware security methods are rarely discussed. The inconsistency hampers with the development of a comprehensive solution for hardware IC and IP from various attacks. In this paper, we develop a security primitive generator to help solve the compatibility issue among different protection techniques. Specifically, we focus on two modern IC/IP protection methods, logic locking and watermarking. A combined locking and watermarking technique is developed based on enhanced finite state machines (FSMs). The security primitive generator will take user-specified constraints and automatically generate an FSM module to perform both logic locking and watermarking. The generated FSM can be integrated into any designs for protection. Our experimental results show that the generator can facilitate circuit protection and provide the flexibility for users to achieve a better tradeoff between security levels and design overheads. 

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2. SecDeep: Secure and Performant On-device Deep Learning Inference Framework for Mobile and IoT Devices

   https://doi.org/10.1145/3450268.3453524  

   Liu, Renju; Garcia, Luis; Liu, Zaoxing; Ou, Botong; Srivastava, Mani (May 2021, IoTDI '21: Proceedings of the International Conference on Internet-of-Things Design and Implementation)

   null (Ed.)

   There is an increasing emphasis on securing deep learning (DL) inference pipelines for mobile and IoT applications with privacy-sensitive data. Prior works have shown that privacy-sensitive data can be secured throughout deep learning inferences on cloud-offloaded models through trusted execution environments such as Intel SGX. However, prior solutions do not address the fundamental challenges of securing the resource-intensive inference tasks on low-power, low-memory devices (e.g., mobile and IoT devices), while achieving high performance. To tackle these challenges, we propose SecDeep, a low-power DL inference framework demonstrating that both security and performance of deep learning inference on edge devices are well within our reach. Leveraging TEEs with limited resources, SecDeep guarantees full confidentiality for input and intermediate data, as well as the integrity of the deep learning model and framework. By enabling and securing neural accelerators, SecDeep is the first of its kind to provide trusted and performant DL model inferencing on IoT and mobile devices. We implement and validate SecDeep by interfacing the ARM NN DL framework with ARM TrustZone. Our evaluation shows that we can securely run inference tasks with 16× to 172× faster performance than no acceleration approaches by leveraging edge-available accelerators. 

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3. High-Level Approaches to Hardware Security: A Tutorial

   https://doi.org/10.1145/3577200  

   Pearce, Hammond; Karri, Ramesh; Tan, Benjamin (January 2023, ACM Transactions on Embedded Computing Systems)

   Designers use third-party intellectual property (IP) cores and outsource various steps in the integrated circuit (IC) design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC and pirate the IP. Similarly, if attackers get hold of a design, they can insert malicious circuits or take advantage of “backdoors” in a design. Unintended design bugs can also result in security weaknesses. This tutorial paper provides an introduction to the domain of hardware security through two pedagogical examples of hardware security problems. The first is a walk-through of the scan chain-based side channel attack. The second is a walk-through of logic locking of digital designs. The tutorial material is accompanied by open access digital resources that are linked in this article. 

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4. DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation

   https://doi.org/10.1016/j.softx.2024.101785  

   Ben_Abbes, Ali; Machicao, Jeaneth; Corrêa, Pedro LP; Specht, Alison; Devillers, Rodolphe; Ometto, Jean P; Kondo, Yasuhisa; Mouillot, David (June 2024, SoftwareX)

   Measuring socioeconomic indices at the scale of regions or countries is required in various contexts, in particular to inform public policies. The use of Deep Learning (DL) and Earth Observation (EO) data is becoming increasingly common to estimate specific variables like societal wealth. This paper presents an end- to-end framework ‘DeepWealth’ that calculates such a wealth index using open-source EO data and DL. We use a multidisciplinary approach incorporating satellite imagery, socio-economic data, and DL models. We demonstrate the effectiveness and generalizability of DeepWealth by training it on 24 African countries and deploying it in Madagascar, Brazil and Japan. Our results show that DeepWealth provides accurate and stable wealth index estimates with an 𝑅2 of 0.69. It empowers computer-literate users skilled in Python and R to estimate and visualize well-being-related data. This open-source framework follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, providing data, source code, metadata, and training checkpoints with its source code made available on Zenodo and GitHub. In this manner, we provide a DL framework that is reproducible and replicable. 

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5. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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