Secure scheme for locating disease-causing genes based on multi-key homomorphic encryption

Zhou, Tanping; Li, Wenchao; Yang, Xiaoyuan; Han, Yiliang; Zheng, Shangwen

doi:10.26599/tst.2021.9010006

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…Third, we construct a directed decryption protocol in which the users involved in the homomorphic evaluation can appoint a target user who can obtain the final decryption result. Our experimental results show that compared to the JWB+17 scheme published in the journal Science, our scheme can be used to diagnose polygenic diseases, and the participants only need to upload their encrypted genetic data once, which reduces the communication traffic by a few hundred-fold [16].…”

Section: ) Secure Scheme For Locating Disease-causing Genes Based On ...mentioning

confidence: 97%

The Next Frontier of Security: Homomorphic Encryption in Action

Sabnis,

Mitragotri

2024

IJRASET

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Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

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Section: ) Secure Scheme For Locating Disease-causing Genes Based On ...mentioning

confidence: 97%

The Next Frontier of Security: Homomorphic Encryption in Action

Sabnis,

Mitragotri

2024

IJRASET

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“…In particular, both types can be categorised further into single or multikey FHE/PHE schemes, where data contributed by multiple users are encrypted under a single key (cloud or DR key) or multiple keys (DO's public key), respectively. Some related works [40], [41], [42], [43] implement secure multiplication and division over encrypted data based on single/multi-key FHE. However, the high computation cost of these methods make them impractical for IoT wearable devices.…”

Section: Related Workmentioning

confidence: 99%

Efficient Privacy-Friendly and Flexible Wearable Data Processing With User-Centric Access Control

Jastaniah,

Zhang,

Mustafa

2024

IEEE Access

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With the advent of cloud computing and the vast amount of data produced by IoT wearable devices, outsourcing computation has become a widespread practice in providing health services to individuals and society. Conventional approaches typically focus on either secure data processing or fine-grain access control. Nevertheless, only a limited number of existing solutions consider secure finegrain access control over the encrypted computational results. Notably, these solutions overlook data owners' access control. In addition, they almost exclusively focus on data aggregation operations, neglecting multiplication and division operations on encrypted data, which are fundamental operations with significant importance in various application scenarios. In this paper, we present efficient and privacy-preserving schemes for multiplication and division operations with fine-grain data-sharing and user-centric access control capabilities, called SAMM and SAMD, respectively. We utilise a multi-key Paillier homomorphic cryptosystem to allow privacy-preserving computation of data from both single and multiple data owners. Additionally, we integrate ciphertext-policy attribute-based encryption to enable fine-grain sharing with multiple data requesters based on user-centric access control. Through formal security analysis, we demonstrate that these schemes ensure data confidentiality and authorisation. Moreover, the computational cost and communication overhead of our proposed schemes are thoroughly analysed, and our experimental results indicate that these schemes outperform existing state-of-the-art solutions in terms of efficiency, making them well-suited for use in modern IoT wearable healthcare systems.

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“…The attention layer is used to obtain sentencelevel information, which can extract long-distance dependency information and integrate the coding layer information. The attention layer mainly calculates the attention weight vector r i , which is defined as shown in formula (13).…”

Section: Attention Layermentioning

confidence: 99%

“…Ontology refers to the formal, explicit, and shared conceptual explanation of real world [11]. An ontology is a formal representation of a set of concepts and their relationships in a particular domain [12,13], which is suitable for semantic information representation and inference. Ontology defines constraints that control the simultaneous training of classifiers [14].…”

Section: Introductionmentioning

confidence: 99%

Construction Method of Equipment Defect Knowledge Graph in IoT

Yang,

Zhang

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Equipment defect detection is essential to the security and stability of power grid networking operations. Besides the status of the power grid itself, environmental information is also necessary for equipment defect detection. At the same time, different types of intelligent sensors can monitor environmental information, such as temperature, humidity, dust, etc. Therefore, we apply the Internet of Things (IoT) technology to monitor the related environment and pervasive interconnections to diverse physical objects. However, the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems. Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph, which can be used to organize, share, and analyze equipment defect-related knowledge. At present, there are a lot of relevant data in the field of intelligent equipment defects. These equipment defect data often focus on a single aspect of the defect field. It is difficult to integrate the database with various types of equipment defect information. This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology. Based on ontology, this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities. This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field. The final experiment proves that this model is superior to other models in precision, recall, and F1 value. This research can break the barrier of multi-source heterogeneous knowledge, build an efficient storage engine for multimodal data, and empower the safety of Industrial applications, data, and platforms in multi-clouds for Internet of Things.

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Secure scheme for locating disease-causing genes based on multi-key homomorphic encryption

Cited by 16 publications

References 16 publications

The Next Frontier of Security: Homomorphic Encryption in Action

The Next Frontier of Security: Homomorphic Encryption in Action

Efficient Privacy-Friendly and Flexible Wearable Data Processing With User-Centric Access Control

Construction Method of Equipment Defect Knowledge Graph in IoT

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