Stochastic Mixed-Signal Circuit Design for In-Sensor Privacy
Article No.: 11, Pages 1 - 9
Abstract
The ubiquitous data acquisition and extensive data exchange of sensors pose severe security and privacy concerns for the end-users and the public. To enable real-time protection of raw data, it is demanding to facilitate privacy-preserving algorithms at data generation, or in-sensory privacy. However, due to the severe sensor resource constraints and intensive computation/security cost, it remains an open question of how to enable data protection algorithms with efficient circuit techniques. To answer this question, this paper discusses the potential of a stochastic mixed-signal (SMS) circuit for ultra-low-power, small-foot-print data security. In particular, this paper discusses digitally-controlled-oscillators (DCO) and their advantages in (1) seamless analog interface, (2) stochastic computation efficiency, and (3) unified entropy generation over conventional digital circuit baselines. With DCO as an illustrative case, we target (1) SMS privacy-preserving architecture definition and systematic SMS analysis on its performance gains across various hardware/software configurations, and (2) revisit analog/mixed-signal voltage/transistor scaling in the context of entropy-based data protection.
References
[1]
Q. Yang, S. Fu, H. Wang, and H. Fang, "Machine-Learning-Enabled Co-operative Perception for Connected Autonomous Vehicles: Challenges and Opportunities," IEEE Network, vol. 35, pp. 96--101, May 2021.
[2]
E. Azari and S. Vrudhula, "An Energy-Efficient Reconfigurable LSTM Accelerator for Natural Language Processing," in 2019 IEEE International Conference on Big Data (Big Data), pp. 4450--4459, Dec. 2019.
[3]
Y. Ismail, M. Hammad, and W. El-Medany, "Homeland Security Video Surveillance System for Smart Cities," in 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1--4, Nov. 2018.
[4]
X. Liu, L. Xie, Y. Wang, J. Zou, J. Xiong, Z. Ying, and A. V. Vasilakos, "Privacy and Security Issues in Deep Learning: A Survey," IEEE Access, vol. 9, pp. 4566--4593, 2021.
[5]
M. Rigaki and S. García, "A survey of privacy attacks in machine learning," ArXiv, vol. abs/2007.07646, 2020.
[6]
A. Al Badawi, Y. Polyakov, K. M. M. Aung, B. Veeravalli, and K. Rohloff, "Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme," IEEE Transactions on Emerging Topics in Computing, vol. 9, pp. 941--956, Apr. 2021.
[7]
I. Yoon, N. Cao, A. Amaravati, and A. Raychowdhury, "A 55nm 50nJ/encode 13nJ/decode Homomorphic Encryption Crypto-Engine for IoT Nodes to Enable Secure Computation on Encrypted Data," in 2019 IEEE Custom Integrated Circuits Conference (CICC), pp. 1--4, Apr. 2019.
[8]
N. Samardzic, A. Feldmann, A. Krastev, S. Devadas, R. Dreslinski, C. Peikert, and D. Sanchez, "F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption," in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO '21, pp. 238--252, Association for Computing Machinery, Oct. 2021. event-place: New York, NY, USA.
[9]
S. Gupta, R. Cammarota, and T. Rosing, "MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory," arXiv:2204.12557 [cs], Apr. 2022. arXiv: 2204.12557.
[10]
C. Xu, J. Ren, Y. Zhang, Z. Qin, and K. Ren, "DPPro: Differentially Private High-Dimensional Data Release via Random Projection," Trans. Info. For. Sec., vol. 12, pp. 3081--3093, Dec. 2017.
[11]
B. Khaleghi, M. Imani, and T. Rosing, "Prive-hd: Privacy-preserved hyperdimensional computing," in 2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1--6, 2020.
[12]
M. A. P. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, "An efficient and scalable privacy preserving algorithm for big data and data streams," Computers & Security, vol. 87, p. 101570, Nov. 2019.
[13]
B. Denham, R. Pears, and M. A. Naeem, "Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining," Expert Systems with Applications, vol. 152, p. 113380, Aug. 2020.
[14]
M. Showkatbakhsh, C. Karakus, and S. Diggavi, "Privacy-Utility Tradeoff of Linear Regression under Random Projections and Additive Noise," 2018 IEEE International Symposium on Information Theory (ISIT), pp. 186--190, June 2018. arXiv: 1902.04688.
[15]
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou, "A Hybrid Approach to Privacy-Preserving Federated Learning," in Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec'19, pp. 1--11, Association for Computing Machinery, Nov. 2019. event-place: New York, NY, USA.
[16]
K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. S. Quek, and H. V. Poor, "Federated Learning With Differential Privacy: Algorithms and Performance Analysis," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3454--3469, 2020.
[17]
Z. He, T. Zhang, and R. B. Lee, "Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems," IEEE Internet of Things Journal, vol. 8, pp. 9706--9716, June 2021.
[18]
W. Miao, Z. Zeng, L. Wei, S. Li, C. Jiang, and Z. Zhang, "Adaptive DNN Partition in Edge Computing Environments," in 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 685--690, Dec. 2020.
[19]
"General Data Protection Regulation (GDPR) Compliance Guidelines, gdpr.eu." [online] Available: https://gdpr.eu/, [Accessed 10-May-2022].
[20]
"Privacy by Design." [online] Available: https://gdpr-info.eu/issues/privacy-by-design/ [Accessed 10-May-2022].
[21]
J. Zouari, M. Hamdi, and T.-H. Kim, "A privacy-preserving homomorphic encryption scheme for the Internet of Things," in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1939--1944, June 2017.
[22]
D. Bankman, L. Yang, B. Moons, M. Verhelst, and B. Murmann, "An Always-On 3.8 J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS," IEEE Journal of Solid-State Circuits, vol. 54, pp. 158--172, Jan. 2019.
[23]
T.-H. Hsu, Y.-K. Chen, T.-H. Wen, W.-C. Wei, Y.-R. Chen, F.-C. Chang, H. Kim, Q. Chen, B. Kim, R.-S. Liu, C.-C. Lo, K.-T. Tang, M.-F. Chang, and C.-C. Hsieh, "A 0.5V Real-Time Computational CMOS Image Sensor with Programmable Kernel for Always-On Feature Extraction," in 2019 IEEE Asian Solid-State Circuits Conference (A-SSCC), pp. 33--34, Nov. 2019.
[24]
M. Yang, C.-H. Yeh, Y. Zhou, J. P. Cerqueira, A. A. Lazar, and M. Seok, "A 1W voice activity detector using analog feature extraction and digital deep neural network," in 2018 IEEE International Solid - State Circuits Conference - (ISSCC), pp. 346--348, Feb. 2018.
[25]
N. Cao, M. Chang, and A. Raychowdhury, "14.1 A 65nm 1.1-to-9.1TOPS/W Hybrid-Digital-Mixed-Signal Computing Platform for Accelerating Model-Based and Model-Free Swarm Robotics," in 2019 IEEE International Solid- State Circuits Conference - (ISSCC), pp. 222--224, Feb. 2019. ISSN: 2376-8606.
[26]
L. Jiang, R. Tan, X. Lou, and G. Lin, "On Lightweight Privacy-preserving Collaborative Learning for Internet of Things by Independent Random Projections," ACM Trans. Internet Things, vol. 2, pp. 11:1--11:32, Mar. 2021.
[27]
J. Matoušek, "On variants of the Johnson-Lindenstrauss lemma," Random Structures & Algorithms, vol. 33, no. 2, pp. 142--156, 2008.
[28]
D. G. Chen, F. Tang, M.-K. Law, X. Zhong, and A. Bermak, "A 64 fJ/step 9-bit SAR ADC Array With Forward Error Correction and Mixed-Signal CDS for CMOS Image Sensors," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 61, pp. 3085--3093, Nov. 2014.
[29]
N. Cao, M. Chang, and A. Raychowdhury, "A 65-nm 8-to-3-b 1.0-0.36-V 9.1-1.1-TOPS/W Hybrid-Digital-Mixed-Signal Computing Platform for Accelerating Swarm Robotics," IEEE Journal of Solid-State Circuits, pp. 1--11, 2019.
[30]
M. Gong, N. Cao, M. Chang, and A. Raychowdhury, "A 65nm Thermometer-Encoded Time/Charge-Based Compute-in-Memory Neural Network Accelerator at 0.735pJ/MAC and 0.41pJ/Update," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, pp. 1408--1412, Apr. 2021.
[31]
S. Taneja, V. K. Rajanna, and M. Alioto, "36.1 Unified In-Memory Dynamic TRNG and Multi-Bit Static PUF Entropy Generation for Ubiquitous Hardware Security," in 2021 IEEE International Solid- State Circuits Conference (ISSCC), vol. 64, pp. 498--500, Feb. 2021.
[32]
S. Batabyal and A. A. Bazil Rai, "Design of A Ring Oscillator Based PUF with Enhanced Challenge Response pair and Improved Reliability," in 2019 4th International Conference on Recent Trends on Electronics, Information, Communication Technology (RTEICT), pp. 1370--1374, May 2019.
[33]
J. Park and J.-Y. Sim, "36.4 A Physically Unclonable Function Combining a Process Mismatch Amplifier in an Oscillator Collapse Topology," in 2021 IEEE International Solid- State Circuits Conference (ISSCC), vol. 64, pp. 504--506, Feb. 2021.
[34]
Y. Pang, B. Gao, D. Wu, S. Yi, Q. Liu, W.-H. Chen, T.-W. Chang, W.-E. Lin, X. Sun, S. Yu, H. Qian, M.-F. Chang, and H. Wu, "25.2 A Reconfigurable RRAM Physically Unclonable Function Utilizing Post-Process Randomness Source With <6X106 Native Bit Error Rate," in 2019 IEEE International Solid- State Circuits Conference - (ISSCC), pp. 402--404, Feb. 2019. ISSN: 2376-8606.
[35]
D. Roy, I. Chakraborty, T. Ibrayev, and K. Roy, "On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks," Tech. Rep. arXiv:2008.12016, arXiv, Mar. 2021. arXiv: 2008.12016 [cs].
[36]
K. Shamsi and Y. Jin, "Security of emerging non-volatile memories: Attacks and defenses," in 2016 IEEE 34th VLSI Test Symposium (VTS), pp. 1--4, Apr. 2016.
[37]
A. Amravati, S. B. Nasir, S. Thangadurai, I. Yoon, and A. Raychowdhury, "A 55nm time-domain mixed-signal neuromorphic accelerator with stochastic synapses and embedded reinforcement learning for autonomous micro-robots," in 2018 IEEE International Solid - State Circuits Conference - (ISSCC), pp. 124--126, Feb. 2018.
[38]
M. Handa, H. Bhasin, S. Dwari, S. Kumar, and B. K. Kanaujia, "Analysis of threshold voltage variation using stacked-FET power amplifiers," in 2014 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1--4, 2014.
Index Terms
- Stochastic Mixed-Signal Circuit Design for In-Sensor Privacy
Index terms have been assigned to the content through auto-classification.
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October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
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