A Framework for Scalable Object Storage and Retrieval Considering Privacy Concerns: A Case Study on the Signature Detection

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Daliri Khomami Mohammad Mehdi; Saghiri Ali Mohammad; Rezvanian Alireza; MEYBODI MOHAMMAD REZA

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

A Framework for Scalable Object Storage and Retrieval Considering Privacy Concerns: A Case Study on the Signature Detection

Author(s)

Daliri Khomami Mohammad Mehdi | Saghiri Ali Mohammad | Rezvanian Alireza | MEYBODI MOHAMMAD REZA

Keywords

Content-Addressable Network

Siamese Neural Networks

Vector Space Model

Scalability

Privacy

Abstract

Nowadays, we are faced with a huge amount of private data generated in different ecosystems, including the Internet of Things, social networks, peer-to-peer networks, and e-commerce, to mention a few. The performance of traditional object storage and retrieval systems concerning response time is going to be degraded by a drastic increase in generated data. This problem becomes more challenging when a specific private object should be found. To address the mentioned problem, this paper suggests scalable object storage and retrieval. To do this, three well-known methods, Content-Addressable Networks from peer-to-peer systems, Vector Space Model from information retrieval systems, and Siamese Neural Networks from neural networks theory, are involved in a framework to bring a scalable system to solve the mentioned problems. The suggested framework is unique to the best of our knowledge because there is no fusion among mentioned fields reported so far. A case study on signature detection is also conducted to evaluate the proposed framework. The results show that, compared to a centralized system, the proposed framework significantly decreases the response time for detecting a signature while maintaining the same accuracy. In addition, the proposed mechanism does not require the private objects to be sent to a central entity, which helps to alleviate Privacy concerns.

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APA: Copy

Daliri Khomami, Mohammad Mehdi, Saghiri, Ali Mohammad, Rezvanian, Alireza, & MEYBODI, MOHAMMAD REZA. (). . . SID. https://sid.ir/paper/1046859/en

Vancouver: Copy

Daliri Khomami Mohammad Mehdi, Saghiri Ali Mohammad, Rezvanian Alireza, MEYBODI MOHAMMAD REZA. . . Available from: https://sid.ir/paper/1046859/en

IEEE: Copy

Mohammad Mehdi Daliri Khomami, Ali Mohammad Saghiri, Alireza Rezvanian, and MOHAMMAD REZA MEYBODI, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046859/en

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