MemFHE: End-to-end Computing with Fully Homomorphic Encryption in Memory

Gupta, Saransh; Cammarota, Rosario; Rosing, Tajana

doi:10.1145/3569955

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…Meanwhile, the BGV scheme needs to control the noise growth by using modulus switching [30]. Unlike the above schemes, the ciphertext form of GSW is a matrix [31]. It does not have the problem of ciphertext multiplication dimension growth.…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption

Zhang,

Lu,

Qiu

et al. 2023

Entropy

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Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.

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Section: Homomorphic Encryptionmentioning

confidence: 99%

Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption

Zhang,

Lu,

Qiu

et al. 2023

Entropy

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“…The variety of supported operations allow for a wide range of computations to be performed on encrypted data, making FHE powerful and versatile and applicable in multiple settings. In Cloud Computing, FHE is used to protect the client's data privacy to process them on an external party [33], [34]. The line of FHE works on Machine Learning aim to protect the training data's privacy in either a collaborative setting [35], [36] or a federated learning setting [37], [38].…”

Section: B Fully Homomorphic Encryptionmentioning

confidence: 99%

Is Homomorphic Encryption Feasible for Smart Mobility?

Hannemann,

Buchmann

2023

Annals of Computer Science and Information Systems

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Smart mobility is a promising approach to meet urban transport needs in an environmentally and and user-friendly way. Smart mobility computes itineraries with multiple means of transportation, e.g., trams, rental bikes or electric scooters, according to customer preferences. A mobility platform cares for reservations, connecting transports, invoicing and billing. This requires sharing sensible personal data with multiple parties, and puts data privacy at risk.In this paper, we investigate if fully homomorphic encryption (FHE) can be applied in practice to mitigate such privacy issues. FHE allows to calculate on encrypted data, without having to decrypt it first. We implemented three typical distributed computations in a smart mobility scenario with SEAL, a recent programming library for FHE. With this implementation, we have measured memory consumption and execution times for three variants of distributed transactions, that are representative for a wide range of smart mobility tasks. Our evaluation shows, that FHE is indeed applicable to smart mobility: With today's processing capabilities, state-of-the-art FHE increases a smart mobility transaction by about 100 milliseconds and less than 3 microcents.

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