Aggregating privatized medical data for secure querying applications

Singh, Kalpana; Batten, Lynn Margaret

doi:10.1016/j.future.2016.11.028

Cited by 12 publications

(5 citation statements)

References 171 publications

(364 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dataset anonymization via synthetic data generation attempts to balance disclosure risk and data utility in the final synthetic dataset. The goal is to ensure observations are not identifiable and the relevant data mining tasks are not compromised [108,109].…”

Section: Privacymentioning

confidence: 99%

Tabular and latent space synthetic data generation: a literature review

Fonseca

Bação

2023

J Big Data

View full text Add to dashboard Cite

The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data formats and Machine Learning (ML) tasks. However, one of the most common data formats used in industrial applications, tabular data, is generally overlooked; Literature analyses are scarce, state-of-the-art methods are spread across domains or ML tasks and there is little to no distinction among the main types of mechanism underlying synthetic data generation algorithms. In this paper, we analyze tabular and latent space synthetic data generation algorithms. Specifically, we propose a unified taxonomy as an extension and generalization of previous taxonomies, review 70 generation algorithms across six ML problems, distinguish the main generation mechanisms identified into six categories, describe each type of generation mechanism, discuss metrics to evaluate the quality of synthetic data and provide recommendations for future research. We expect this study to assist researchers and practitioners identify relevant gaps in the literature and design better and more informed practices with synthetic data.

show abstract

Section: Privacymentioning

confidence: 99%

Tabular and latent space synthetic data generation: a literature review

Fonseca

Bação

2023

J Big Data

View full text Add to dashboard Cite

show abstract

“…Implementation of two fish encryption algorithm is shown in fig 2 . Fig. 2: Implementation of two fish Cryptographic Implementation steps of Two Fish Cryptographic algorithm is given below [28][29][30]:…”

Section: Implementation Of Two Fish Algorithmmentioning

confidence: 99%

“…To guarantee the security and data privacy over a patient's data, we should implement a smart storage method which include the smart IoT-based healthcare architecture is discussed in [27]. Other solutions for sharing delicate medical data on several methods like medical data accumulation of non-standard diagonal method was suggested in [28], sharing of medical data with a cloud-based model used in [29], a hybrid solution of sharing medical data in [30,31], storage structure of scalable privacy with data preserving scheme [32], a secure system using a fog computation technique in [33], and a distributed based architecture with doubles tag micro aggregation scheme in [34] are implemented. The main problems among these techniques are computational complexity and more time consumption.…”

Section: Introductionmentioning

confidence: 99%

“…Fig 2. Implementation of Two Fish CryptographicImplementation steps of Two Fish Cryptographic algorithm is given below[28][29][30]:Algorithm 1.Step 1: Input block size is 128 bits would be divided into four sections, each for 32 bits words.Step 2:32-Bit word is XOR input with the four key parts. 𝐵 0,𝑖 = 𝑅 ⊕ 𝐾 𝑖 ; 𝑖 = 0 𝑡𝑜 3Where K is a key and 𝐾 𝑖 is a sub key i= 0 to 3.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Two Fish Encryption Based Blockchain Technology for Secured Data Storage

Duraimutharasan

2023

JMC

View full text Add to dashboard Cite

Data security and sharing remains nuisance among many applications like business data, medical data, banking data etc. In this research, block chain technology is built with encryption algorithm for high level data security in cloud storage. Medical data security seems critical aspect due to sensitivity of patient’s information. Unauthorized access of medical data creates major issue to patients. This article proposed block chain with hybrid encryption technique for securing medical data stored in block chain model at cloud storage. New Two fish encryption model is implemented based on RSA Multiple Precision Arithmetic (MPA). MPA works by using library concept. The objective of using this methodology is to enhance security performance with less execution time. Patient data is processed by encryption algorithm and stored at blockchain infrastructure using encrypted key. Access permission allows user to read or write the medical data attached in block chain framework. The performance of traditional cryptographic techniques is very less in providing security infrastructure. Proposed blockchain based Two fish encryption technique provides high security in less encryption and decryption time.

show abstract

“…In 2010 a survey on PPDP [5] discussed common privacy preservation models and their support for different types of attack, anonymisation techniques and information utility metrics. Anonymisation techniques seek to balance the trade-off between disclosure risk and data utility in the final published data, rendering a modified version of the original dataset in such a way that individuals are no longer identifiable [6,7]. However, the utility of data anonymised using these methods is often adversely impacted and the data remains susceptible to disclosure [8].…”

Section: Introductionmentioning

confidence: 99%