More Like this

1. Accelerating Homomorphic Comparison Operations for Thresholding Using an Asymmetric Input Range and Input Scaling

   https://doi.org/10.1145/3649476  

   Kim, Sunwoong; Cho, Wonhee (June 2024, ACM)

   In a cyber-physical system (CPS), the interconnection of cyber and physical components occurs through a network. This structure, particularly cyber components and networks, makes it susceptible to malicious attacks. One of the solutions to this CPS security issue is to employ end-to-end homomorphic encryption (HE) that allows direct computations on encrypted data. Despite its promise, HE only supports basic operations, such as addition and multiplication, which limits its application areas. Numerical methods have been presented to perform a comparison operation in the HE domain. However, they suffer from a slow processing speed due to an inherently high number of iterations. To accelerate a homomorphic comparison operation, this paper introduces a novel approach that scales inputs using an asymmetric input range in thresholding. Additionally, parallelism in HE-based multilevel thresholding is explored and exploited through the use of a parallel processing application programming interface for further acceleration. Compared to a previous comparison operation method, the proposed method achieves comparable accuracy with fewer iterations, resulting in a 48% reduction in execution time on an edge computing device. Furthermore, employing an additional thread using parallelism increases this reduction to 63%. 

   more » « less
2. HEBGS: Homomorphic Encryption-based Background Subtraction Using a Fast-Converging Numerical Method

   https://doi.org/10.1109/ISCAS46773.2023.10181453  

   Shyi, Justin; Kim, Sunwoong (May 2023, 2023 IEEE International Symposium on Circuits and Systems (ISCAS))

   Recent advances in cloud services provide greater computing ability to edge devices on cyber-physical systems (CPS) and internet of things (IoT) but cause security issues in cloud servers and networks. This paper applies homomorphic encryption (HE) to background subtraction (BGS) in CPS/IoT. Cheon et al.'s numerical methods are adopted to implement the non-linear functions of BGS in the HE domain. In particular, square- and square root-based HE-based BGS (HEBGS) designs are proposed for the input condition of the numerical comparison operation. In addition, a fast-converging method is proposed so that the numerical comparison operation outputs more accurate results with lower iterations. Although the outer loop of the numerical comparison operation is removed, the proposed square-based HEBGS with the fast-converging method shows an average peak signal-to-noise ratio value of 20dB and an average structural similarity index measure value of 0.89 compared to the non-HE-based conventional BGS. On a PC, the execution time of the proposed design for each 128×128-sized frame is 0.34 seconds. 

   more » « less
3. HELPSE: Homomorphic Encryption-based Lightweight Password Strength Estimation in a Virtual Keyboard System

   https://doi.org/10.1145/3526241.3530338  

   Cho, Michael; Lee, Keewoo; Kim, Sunwoong (June 2022, Proceedings of the Great Lakes Symposium on VLSI 2022)

   Recently, cyber-physical systems are actively using cloud servers to overcome the limitations of power and processing speed of edge devices. When passwords generated on a client device are evaluated on a server, the information is exposed not only on networks but also on the server-side. To solve this problem, we move the previous lightweight password strength estimation (LPSE) algorithm to a homomorphic encryption (HE) domain. Our proposed method adopts numerical methods to perform the operations of the LPSE algorithm, which is not provided in HE schemes. In addition, the LPSE algorithm is modified to increase the number of iterations of the numerical methods given depth constraints. Our proposed HE-based LPSE (HELPSE) method is implemented as a client-server model. As a client-side, a virtual keyboard system is implemented on an embedded development board with a camera sensor. A password is obtained from this system, encrypted, and sent over a network to a resource-rich server-side. The proposed HELPSE method is performed on the server. Using depths of about 20, our proposed method shows average error rates of less than 1% compared to the original LPSE algorithm. For a polynomial degree of 32K, the execution time on the server-side is about 5 seconds. 

   more » « less
4. A fast proximal gradient method and convergence analysis for dynamic mean field planning

   https://doi.org/10.1090/mcom/3879  

   Yu, Jiajia; Lai, Rongjie; Li, Wuchen; Osher, Stanley (July 2023, Mathematics of Computation)

   In this paper, we propose an efficient and flexible algorithm to solve dynamic mean-field planning problems based on an accelerated proximal gradient method. Besides an easy-to-implement gradient descent step in this algorithm, a crucial projection step becomes solving an elliptic equation whose solution can be obtained by conventional methods efficiently. By induction on iterations used in the algorithm, we theoretically show that the proposed discrete solution converges to the underlying continuous solution as the grid becomes finer. Furthermore, we generalize our algorithm to mean-field game problems and accelerate it using multilevel and multigrid strategies. We conduct comprehensive numerical experiments to confirm the convergence analysis of the proposed algorithm, to show its efficiency and mass preservation property by comparing it with state-of-the-art methods, and to illustrate its flexibility for handling various mean-field variational problems. 

   more » « less
5. Exploring Bitslicing Architectures for Enabling FHE-Assisted Machine Learning

   https://doi.org/10.1109/TCAD.2022.3204909  

   Sinha, Soumik; Saha, Sayandeep; Alam, Manaar; Agarwal, Varun; Chatterjee, Ayantika; Mishra, Anoop; Khazanchi, Deepak; Mukhopadhyay, Debdeep (November 2022, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   Homomorphic encryption (HE) is the ultimate tool for performing secure computations even in untrusted environments. Application of HE for deep learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g., third-party servers) yet inferring on private data. However, existing HE libraries [somewhat (SWHE), leveled (LHE) or fully homomorphic (FHE)] suffer from extensive computational and memory overhead. Few performance optimized high-speed homomorphic libraries are either suffering from certain approximation issues leading to decryption errors or proven to be insecure according to recent published attacks. In this article, we propose architectural tricks to achieve performance speedup for encrypted DL inference developed with exact HE schemes without any approximation or decryption error in homomorphic computations. The main idea is to apply quantization and suitable data packing in the form of bitslicing to reduce the costly noise handling operation, Bootstrapping while achieving a functionally correct and highly parallel DL pipeline with a moderate memory footprint. Experimental evaluation on the MNIST dataset shows a significant ( 37× ) speedup over the nonbitsliced versions of the same architecture. Low memory bandwidths (700 MB) of our design pipelines further highlight their promise toward scaling over larger gamut of Edge-AI analytics use cases. 

   more » « less