Comparative study of homomorphic encryption methods for secured data operations in cloud computing

Rangasami, Kanagavalli; Vagdevi, S

doi:10.1109/iceeccot.2017.8284566

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…HE enables an application to perform a computation on encrypted data without decrypting the cipher text. This type of encryption allows the delivery of a cipher text C.M / of a plaintext message M and performs the computation on cipher text C.f .M // function on a plaintext message M without decrypting the message M [18,28] . This encryption process proceeds in three steps:…”

Section: Hementioning

confidence: 99%

Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

Jalali,

Chen

2024

Tsinghua Sci. Technol.

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The widespread use of the Internet of Things (IoTs) and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings. Within such systems, all participants related to commercial and industrial systems must communicate and generate data. However, due to the small storage capacities of IoT devices, they are required to store and transfer the generated data to third-party entity called "cloud", which creates one single point to store their data. However, as the number of participants increases, the size of generated data also increases. Therefore, such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security, privacy, and performance.To address these challenges, Federated Learning (FL) has been proposed as a reasonable decentralizing approach, in which clients no longer need to transfer and store real data in the central server. Instead, they only share updated training models that are trained over their private datasets. At the same time, FL enables clients in distributed systems to share their machine learning models collaboratively without their training data, thus reducing data privacy and security challeges. However, slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system. Furthermore, these unnecessary communication rounds make the system vulnerable to security and privacy issues, because irrelevant model updates are sent between clients and servers. Thus, in this work, we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song (CKKS) to encrypt model parameters for their local information privacy-preserving function. The proposed solution uses the impetus term to speed up model convergence during the model training process. Furthermore, it establishes a secure communication channel between IoT devices and the server. We also use a lightweight secure transport protocol to mitigate the communication overhead, thereby improving communication security and efficiency with low communication latency between client and server.

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Section: Hementioning

confidence: 99%

Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

Jalali,

Chen

2024

Tsinghua Sci. Technol.

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“…Away from standard data protection schemes, to improve the performance of encryption, compression and steganography schemes an efficient approach is presented in [8]. The performance of various security schemes are analyzed and compared with the performance of RSA, MD5 and SHA1 approaches [9]. A combined approach is presented in [10], which clubs Byte rotation and ABE schemes to improve performance in peer to peer networks.…”

Section: Related Workmentioning

confidence: 99%

Cloud Data Security in Multi Model Attributes For Randomized Key Service Using Encryption Techniques

S.Noordeen¹

2021

TURCOMAT

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The growing size of information, security became the risk of access from centralized resource providers deploy them into cloud, where authorized users could access them. The nearly combined nature of cloud environment does not yet allow users to perceive the cloud resources and services in a split second. At the same time, they are upgrading. Also, attribute based encryption techniques are used towards the security development in few methods. Also, profile based approaches are used which uses different encryption keys according to user profile. However, all the methods suffer to achieve higher performance in datasecurity. To solve this issue and to improve the security performance, an efficient Service LevelAttributeBasedEncryption (SLABE)ispresented.Inthisapproach,themethoduses different key set of different services. For each attribute, the method maintains different keys for various services. According to the key belongs to the attributes and service, the method performs encryption and decryption. The method improves the performance in security and increases the throughput aswell. Further to improve the security performance, a multi attribute randomized key Service Level Encryption (MARK-SLE) scheme has been presented. In this approach, the method classifies the service and for each service, the method generates different key set according to the attributes accessed. The method selects the keys in a randomized approach and chooses the keys at different time session. Generated key has been used to perform encryption or decryption where the schemes of encryption also selected in a random manner. The proposed MARK-SLE algorithm improves the security performance than previous SLABEalgorithm. Third, a Service Level Scheduler Based Encryption (SLSBE) Scheme is presented. In this approach, the security in service level and scheduling strategy is considered. For each service available, the method maintains the set of attributes being accessed. For each level of service and attributes, the method uses different keys and encryption standards. At the reception of user request, the method identifies the service claimed and set of attributes.

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“…Nevertheless, some encryption algorithms are not applicable for computing ciphertext directly in cloud computing platform. On the contrary, homomorphic encryption can execute operations of ciphertext directly, which called the feature as algebraic homomorphism [16]- [18]. However, as the high computational complexity of homomorphic encryption, many scholars concentrate on how to improve the execution efficiency for sake of the popularity of the cloud computing platform [19].…”

Section: Related Workmentioning

confidence: 99%

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Xia¹

2019

JCP

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As an important encryption algorithm of performing computations directly on ciphertext without any need of decryption and compromising privacy, homomorphic encryption is an increasingly popular research topic of protecting privacy of the data in cloud security research. However, it will be a heavy workload for resources as the high computational complexity of homomorphic encryption. Therefore, GPU acceleration is employed to speed up homomorphic encryption. Motivated by this observation, we utilize parallel computing mode in DGHV algorithm with GPUs acceleration based on CPU-GPUs hybrid system. Our main contribution is to present a parallel computing mode for large-scale data encryption based on CPU-GPUs hybrid system as fast as possible. Specifically, we applies parallel computing mode in DGHV with GPUs acceleration to reduce the time duration and provide a comparative performance study. We further design pipeline architecture of processing stream to accelerate the speed of DGHV algorithm. Furthermore, experimental results validate that different parallelism has the corresponding granularity in parallel computing mode. Experimental results show that our method gains more than 84% improvement (run time), 67% improvement (run time), and 80% improvement (run time) compared to the sequential data encryption, sequential homomorphic addition, and sequential homomorphic multiplication in DGHV algorithm respectively.

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Comparative study of homomorphic encryption methods for secured data operations in cloud computing

Cited by 14 publications

References 12 publications

Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

Cloud Data Security in Multi Model Attributes For Randomized Key Service Using Encryption Techniques

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

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