Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Feng, Xiaoqin; Ma, Jianfeng; Liu, Shaobin; Miao, Yinbin; Liu, Ximeng; Choo, Kim‐Kwang Raymond

doi:10.1109/jiot.2021.3100097

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Several additional functionalities in encrypted data use have seen improvement without exhaustive enumeration. This encompasses semantics (words with similar meanings) [23], multiple data owners collaborating with multiple users [43], deduplication of data [44], comparison of multi-dimensional data between users without revealing the data [9], querying encrypted datasets with heterogeneous data sources [45], and clustering encrypted unstructured big data [46].…”

Section: Other Functionalitiesmentioning

confidence: 99%

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Kishiyama,

Alsmadi

2024

IJSTS

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Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

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Section: Other Functionalitiesmentioning

confidence: 99%

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Kishiyama,

Alsmadi

2024

IJSTS

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“…DBMS have been fundamental in organizing and retrieving information for years (Aldwairi et al, 2023). The birth of this is traced back to the 1960s when hierarchical and network models were introduced, establishing the basis for organized data storage and retrieval (Feng et al, 2021). The relational paradigm, which emerged in the 1970s and was advocated by E.F. Codd, brought about a significant transformation in data management.…”

Section: Introduction To Database Management System and Solutionsmentioning

confidence: 99%

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Clavijo-López,

Navarrete,

Velásquez

et al. 2024

JISIS

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Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

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A Mobile Data Leakage Prevention System Based on Encryption Algorithms

Shen

Xiong²

2023

Communications in Computer and Information Science

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Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Cited by 5 publications

References 36 publications

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

A Mobile Data Leakage Prevention System Based on Encryption Algorithms

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