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We consider an architecture of confidential cloud-based control synthesis based on Homomorphic Encryption (HE). Our study is motivated by the recent surge of data-driven control such as deep reinforcement learning, whose heavy computational requirements often necessitate an outsourcing to the third party server. To achieve more flexibility than Partially Homomorphic Encryption (PHE) and less computational overhead than Fully Homomorphic Encryption (FHE), we consider a Reinforcement Learning (RL) architecture over Leveled Homomorphic Encryption (LHE). We first show that the impact of the encryption noise under the Cheon-Kim-Kim-Song (CKKS) encryption scheme on the convergence of the model-based tabular Value Iteration (VI) can be analytically bounded. We also consider secure implementations of TD(0), SARSA(0) and Z-learning algorithms over the CKKS scheme, where we numerically demonstrate that the effects of the encryption noise on these algorithms are also minimal.
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Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs
Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine learning. However, high latency in large integer operations in the CKKS algorithm hinders the processing of large datasets and complex computations. This paper proposes a novel strategy that combines lossless data compression techniques with the parallel processing power of graphics processing units to address these challenges. Our approach demonstrably reduces data size by 90% and achieves significant speedups of up to 100 times compared to conventional approaches. This method ensures data confidentiality while mitigating performance bottlenecks in CKKS-based computations, paving the way for more efficient and scalable HE applications.
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- Award ID(s):
- 2348464
- PAR ID:
- 10579462
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Conference proceedings
- ISSN:
- 1558-3899
- ISBN:
- 979-8-3503-8717-9
- Page Range / eLocation ID:
- 1145 to 1149
- Format(s):
- Medium: X
- Location:
- Springfield, MA, USA
- Sponsoring Org:
- National Science Foundation
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Fully Homomorphic Encryption (FHE) presents a paradigm-shifting framework for performing computations on encrypted data, offering revolutionary implications for privacy-preserving technologies. This paper introduces a novel hardware implementation of scheme switching between two leading FHE schemes targeting different computational needs, i.e., arithmetic HE scheme CKKS, and Boolean HE scheme FHEW. The proposed architecture facilitates dynamic switching between the schemes with improved throughput and latency compared to the software baseline. The proposed architecture computation modules support scheme switching operations involving coefficient conversion, modular switching, and key switching. We also optimize the hardware designs for the pre-processing and post-processing blocks, involving key generation, encryption, and decryption. The effectiveness of our proposed design is verified on the Xilinx U280 Datacenter Acceleration FPGA. We demonstrate that the proposed scheme switching accelerator yields a 365× performance improvement over the software counterpart.more » « less
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Homomorphic encryption enables computations on the ciphertext to preserve data privacy. However, its practical deployment has been hindered by the significant computational overhead compared to the plaintext computations. In response to this challenge, we present HERMES, a novel hardware acceleration system designed to explore the computation flow of the CKKS homomorphic encryption bootstrapping process. Among the major contributions of our proposed architecture, we first analyze the properties of the CKKS computation data flow and propose a new scheduling strategy by partitioning the computation modules into general-purpose and special-purpose modular computation modules to allow smaller resource consumption and flexible scheduling. The computation modules are also reconfigurable to reduce the memory access overhead during the intermediate computation. We also optimize the CKKS computation dataflow to improve the regularity with reduced control overhead.more » « less
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Canteaut, A.; Standaert, F. (Ed.)We present passive attacks against CKKS, the homomorphic encryption scheme for arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both theoretically efficient (running in expected polynomial time) and very practical, leading to complete key recovery with high probability and very modest running times. We implemented and tested the attack against major open source homomorphic encryption libraries, including HEAAN, SEAL, HElib and PALISADE, and when computing several functions that often arise in applications of the CKKS scheme to machine learning on encrypted data, like mean and variance computations, and approximation of logistic and exponential functions using their Maclaurin series. The attack shows that the traditional formulation of IND-CPA security (or indistinguishability against chosen plaintext attacks) achieved by CKKS does not adequately capture security against passive adversaries when applied to approximate encryption schemes, and that a different, stronger definition is required to evaluate the security of such schemes. We provide a solid theoretical basis for the security evaluation of homomorphic encryption on approximate numbers (against passive attacks) by proposing new definitions, that naturally extend the traditional notion of IND-CPA security to the approximate computation setting. We propose both indistinguishability-based and simulation-based variants, as well as restricted versions of the definitions that limit the order and number of adversarial queries (as may be enforced by some applications). We prove implications and separations among different definitional variants, and discuss possible modifications to CKKS that may serve as a countermeasure to our attacks.more » « less
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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
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MWSCAS60917.2024.10658747
