Numeric To Numeric Encryption of Databases: Using 3Kdec Algorithm

Kaur, Kamaljit; Dhindsa, Kanwalvir Singh; Singh, Ghanaya

doi:10.1109/iadcc.2009.4809240

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…(Goel & Hong, 2015) 3KDEC:To address the issue of converting numeric data into alphanumeric format and storing encrypted data in preexisting numeric fields, a symmetric key block encipherment algorithm is employed as a pragmatic solution. (K. Kaur, Dhindsa, & Singh, 2009) Privacy Protection techniques in big data: Big data platforms represent a convergence of various technological advancements pertaining to storage and processing capabilities. Consequently, the conventional security techniques employed in traditional systems prove ineffective and inadequate when directly applied in the context of big data.…”

Section: Endpointmentioning

confidence: 99%

Big Data Security, Privacy Protection, Tools and Applications

A. Shahzad,

Kayani,

A.A.Malik

et al. 2023

PJS

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Innovative and sophisticated technologies have been rapidly developing in recent years. These cutting-edge advancements encompass a wide spectrum of devices like mobile phones, PCs and social media trackers. As a consequence of their widespread usage, these technologies have engendered the generation of vast volumes of unstructured data in diverse formats, spanning terabytes (TB) to petabytes (PB).This vast and varied data is called big data. It holds great promise for both public and private industries. Many organizations utilize big data to uncover useful insights, whether for marketing choices, monitoring specific actions, or identifying potential threats.This kind of data processing is made possible using different methods known as Big Data Analytics. It allows you to gain significant advantages by handling large amounts of unorganized, organized, and partially organized information quickly, which would be impossible with traditional database techniques. While big data presents considerable While it offers benefits for businesses and decision-makers, it also puts consumers at risk. This risk results from the use of analytics technologies, which need the preservation, administration, and thorough analysis of enormous volumes of data gathered from many sources. Consequently, individuals face the risk of their personal information being compromised as a result of the collection and revelation of behavioral data. Put simply, the excessive accumulation of data may lead to multiple breaches in security and privacy. Nevertheless, the realm of big data does indeed raise concerns pertaining to security and privacy. Scholars from various disciplines are actively engaged in addressing these concerns.The study will concentrate on large data applications, substantial security hurdles, and privacy concerns. We'll talk about potential methods for enhancing confidentiality and safety in problematic big data scenarios, and we'll also analyze present security practices

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Section: Endpointmentioning

confidence: 99%

Big Data Security, Privacy Protection, Tools and Applications

A. Shahzad,

Kayani,

A.A.Malik

et al. 2023

PJS

View full text Add to dashboard Cite

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“…If floating-point numbers are encrypted as strings, basic arithmetic calculations on strings are not supported. In addition, if using 3Kdec algorithm to realize numeric to numeric encryption for easily storing [17], there is no indication of enabling the arithmetic calculations on encrypted data as well. The method to expand the homomorphic encryption on the integer to the decimal by 'similar modular arithmetic' is proposed [18].…”

Section: Related Workmentioning

confidence: 99%

Query with SUM Aggregate Function on Encrypted Floating-Point Numbers in Cloud

2017

J Inf Process Syst

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Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

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“…They further proposed a database encryption, according to which an database can be provided as a service. A feature which they proposed was to save th the encrypted data in order to enhance the sec [16] sed an encryption ption. This scheme security.…”

Section: A Standalone Encryption Approachesmentioning

confidence: 99%