A traceability set up using Digitalization of Data and Accessibility

J, Jasmine; Jenipher, V. Nisha; Jimreeves, J. S. Richard; Ravindran, K.; Dhinakaran, D.

doi:10.1109/iciss49785.2020.9315938

Cited by 26 publications

(8 citation statements)

References 7 publications

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“…Furthermore, the suggested data aggregation approach has been proven secure, and it meets all security standards. Finally, the suggested scheme's performance assessment demonstrates its minimal computing expense and transmission delay compared to other relevant systems [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Murugeshwari¹,

Selvaraj²,

Sudharson³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Protecting the privacy of data in the multi-cloud is a crucial task. Data mining is a technique that protects the privacy of individual data while mining those data. The most significant task entails obtaining data from numerous remote databases. Mining algorithms can obtain sensitive information once the data is in the data warehouse. Many traditional algorithms/techniques promise to provide safe data transfer, storing, and retrieving over the cloud platform. These strategies are primarily concerned with protecting the privacy of user data. This study aims to present data mining with privacy protection (DMPP) using precise elliptic curve cryptography (PECC), which builds upon that algebraic elliptic curve in finite fields. This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques. Also, it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data. Average approximation error, computational cost, anonymizing time, and data loss are considered performance measures. The suggested approach is practical and applicable in real-world situations according to the experimental findings.

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Section: Related Workmentioning

confidence: 99%

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Murugeshwari¹,

Selvaraj²,

Sudharson³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…These are usually helpful at eliminating information distortion. In addition, the properties of randomly generated structures like graphs captivate me [28][29][30]. Randomness seems to have a "pattern," If we are not careful, we can leverage this pattern to compromise privacy.…”

Section: System Modelmentioning

confidence: 99%

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Murugeshwari¹,

Rajalakshmi²,

Sudharson³

2023

Computer Systems Science and Engineering

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Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.

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“…After sharing exchange-keys, A and B generate keys for one another. Because A and B do not have to perform any costly path discovery procedures for routing, this key exchange mechanism can now be used directly in packet forwarding [41][42][43][44].…”

Section: Security Modelmentioning

confidence: 99%

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

Pari¹,

Sudharson²

2023

Computer Systems Science and Engineering

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A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network (MANET). A MANET's nodes could engage actively and dynamically with one another. However, MAN-ETs, from the other side, are exposed to severe potential threats that are difficult to counter with present security methods. As a result, several safe communication protocols designed to enhance the secure interaction among MANET nodes. In this research, we offer a reputed optimal routing value among network nodes, secure computations, and misbehavior detection predicated on node's trust levels with a Hybrid Trust based Reputation Mechanism (HTRM). In addition, the study designs a robust Public Key Infrastructure (PKI) system using the suggested trust evaluation method in terms of "key" generation, which is a crucial component of a PKI cryptosystem. We also concentrate on the solid node authenticating process that relies on pre-authentication. To ensure edge-to-edge security, we assess safe, trustworthy routes to secure computations and authenticate mobile nodes, incorporating uncertainty into the trust management solution. When compared to other protocols, our recommended approach performs better. Finally, we use simulations data and performance evaluation metrics to verify our suggested approach's validity Our approach outperformed the competing systems in terms of overall end-to-end delay, packet delivery ratio, performance, power consumption, and key-computing time by 3.47%, 3.152%, 2.169%, and 3.527%, 3.762%, significantly.

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A traceability set up using Digitalization of Data and Accessibility

Cited by 26 publications

References 7 publications

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

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