Efficient Leveled (Multi) Identity-Based Fully Homomorphic Encryption Schemes

Shen, Tongchen; Wang, Fuqun; Chen, Kefei; Wang, Kunpeng; Li, Bao

doi:10.1109/access.2019.2922685

Cited by 26 publications

(20 citation statements)

References 53 publications

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“…Finally, we get the most important decryption invariant in all GSW-like encryption from equations (28)- (30): 31in mind, everything turns familiar again. Homomorphic properties are obvious, and one can find them in most GSW-like FHE (or MKFHE) schemes.…”

Section: Correctness and Homomorphismmentioning

confidence: 98%

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Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

Shen

Wang

Chen

et al. 2021

Security and Communication Networks

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With the development of new computing models such as cloud computing, user’s data are at the risk of being leaked. Fully homomorphic encryption (FHE) provides a possible way to fundamentally solve the problem. It enables a third party who does not know anything about the secret key and plaintexts to homomorphically perform any computable functions on the corresponding ciphertexts. In 2009, Gentry proposed the first FHE scheme. After that, its inefficiency has always been a bottleneck of the development of practical schemes and applications. At TCC 2019, Gentry and Halevi proposed the first compressible FHE scheme that enables the ratio of plaintext size to the ciphertext size (i.e., the compression rate) to reach 1 − ε for any small ε > 0 under the standard learning with errors (LWE) assumption. However, it is only a single-key one, where the homomorphic evaluation can only be performed over ciphertexts encrypted under the same key. Compared with single-key FHE, multikey FHE is more practical. Multikey FHE enables ciphertexts encrypted under different public keys to be homomorphically computed without having to decrypt these ciphertexts using their own private keys. In addition, in a multi-identity FHE scheme, only identity information and public parameters are required when encrypting, which simplifies certificate-based key management in public key infrastructure. In this paper, a new compressible ciphertext expansion technique is proposed. Then, we use this technique to construct a compressible multikey FHE scheme and a compressible multi-identity FHE scheme to overcome the bottleneck of bandwidth inefficiency in the multikey and multi-identity settings. The two schemes proposed in this paper make it possible that the objects of homomorphic operation can be the ciphertexts encrypted under different keys or different identities before compression, thus solving the single-key defect of the work of Gentry and Halevi.

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Section: Correctness and Homomorphismmentioning

confidence: 98%

“…Remark 1. e setting of error parameters, especially E (2) i , plays a critical role in the security proof. For more information, please refer to [30,31].…”

Section: Public Parametersmentioning

confidence: 99%

Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

Shen

Wang

Chen

et al. 2021

Security and Communication Networks

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“…A lightweight encryption scheme is one of the most important methods in privacy protection of IoT. It commonly uses encryption methods like homomorphic encryption [10] and secure multi-party computation (SMC) [11]. The invisibility of the original data and losslessness of the data are realized by the cryptography mechanism.…”

Section: A Data Privacy Protection In Iotmentioning

confidence: 99%

“…Laplace mechanism [10] and exponential mechanism [33] are two basic differential privacy protection mechanisms. Among them, Laplace mechanism is suitable for the protection of numerical results, and exponential mechanism is suitable for non-numerical results.…”

Section: Differential Privacymentioning

confidence: 99%

DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

Liu

Huang

et al. 2019

IEEE Access

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With the advent of the era of intelligent Internet of Things (IoT), more and more personal information are collected in the process of deploying IoT in various domains. This brings the problem of data protection in IoT. To address this problem, we propose a data protection algorithm, called DynaPro, based on differential privacy for dynamic wireless sensor networks (WSN), which is the sensor layer of IoT. DynaPro has addressed two issues in data protection of wireless sensor networks. The first issue is the dynamic topology of the network. In order to solve this problem, DynaPro has adopted methods like time window, hierarchical sampling, and graph similarity to process the snapshots of the dynamic network topology. The second issue is addition of noise in differential privacy. To address this issue, DynaPro uses Hierarchical Random Graph (HRG) as middleware to apply differential privacy protection. Instead of adding noise to network data and topology directly, DynaPro adds noise to HRG. In this way, DynaPro can hide the network topology but retain the necessary structural information to support data analysis. Theoretical analysis and experimental results show that DynaPro can preserve the important network features of the original network topology under the premise of the differential privacy protection model.

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“…HE allows calculations to be performed directly on ciphertext data without decrypting it; operations on encrypted data produce the same results as those obtained with its corresponding plaintext operation. HE is categorized into either partial or full forms: Partially Homomorphic Encryption (PHE) supports addition or multiplication operations, while Fully Homomorphic Encryption (FHE) supports a random number of both operations [4]. It encrypts data and meanwhile supports arbitrary computations on encrypted data, and hence is well suited for cloud storage outsourcing scenarios [5].…”

Section: Introductionmentioning

confidence: 99%

A new fully homomorphic cryptosystem based on a super-increasing sequence

Mohammed¹,

Fadhil²

2020

Telfor J

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Cloud computing is a powerful computing paradigm that provides a variety of computing services to its users. An example is storage, which allows individuals and enterprises to outsource their files to remote storage. However, saving private information onto third-party storage increases the security issues of data and privacy protection concerns. For this reason, cloud service providers (CSPs) are required to save an encrypted version of user data. In this paper, a novel encryption technique based on the use of Fully Homomorphic Encryption is presented. The technique uses a superincreasing sequence to derive the key and works on encrypted data with no need for decryption; this yields the same results as performing it on plaintext data. In the proposed technique, the characters are converted to their corresponding ASCII code values, which differs from the binary values produced by other existing techniques.

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Efficient Leveled (Multi) Identity-Based Fully Homomorphic Encryption Schemes

Cited by 26 publications

References 53 publications

Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

A new fully homomorphic cryptosystem based on a super-increasing sequence

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