Efficient privacy preservation technique for healthcare records using big data

Sathya, S.; Sethukarasi, T.

doi:10.1109/icices.2016.7518878

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…It guarantees that errors in one clause do not prevent the record to be correctly grouped, resulting in a relatively small number of blocks of moderate sizes. Anonymization is a critical issue for health data and different privacy-preserving techniques can be used to address this problem [30]- [32]. AtyImo uses Bloom filters [33], which are binary vectors of size n initialized with 0 (zero).…”

Section: A Data Preprocessingmentioning

confidence: 99%

On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort

Pita

Pinto

Sena

et al. 2018

IEEE J. Biomed. Health Inform.

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Data linkage refers to the process of identifying and linking records that refer to the same entity across multiple heterogeneous data sources. This method has been widely utilized across scientific domains, including public health where records from clinical, administrative, and other surveillance databases are aggregated and used for research, decision making, and assessment of public policies. When a common set of unique identifiers does not exist across sources, probabilistic linkage approaches are used to link records using a combination of attributes. These methods require a careful choice of comparison attributes as well as similarity metrics and cutoff values to decide if a given pair of records matches or not and for assessing the accuracy of the results. In large, complex datasets, linking and assessing accuracy can be challenging due to the volume and complexity of the data, the absence of a gold standard, and the challenges associated with manually reviewing a very large number of record matches. In this paper, we present AtyImo, a hybrid probabilistic linkage tool optimized for high accuracy and scalability in massive data sets. We describe the implementation details around anonymization, blocking, deterministic and probabilistic linkage, and accuracy assessment. We present results from linking a large population-based cohort of 114 million individuals in Brazil to public health and administrative databases for research. In controlled and real scenarios, we observed high accuracy of results: 93%-97% true matches. In terms of scalability, we present AtyImo's ability to link the entire cohort in less than nine days using Spark and scaling up to 20 million records in less than 12s over heterogeneous (CPU+GPU) architectures.

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Section: A Data Preprocessingmentioning

confidence: 99%

On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort

Pita

Pinto

Sena

et al. 2018

IEEE J. Biomed. Health Inform.

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“…These techniques guarantee privacy preservation while maintaining the usefulness of the data. The goal of S.Sathya et al [21] is to take advantage of the new privacy difficulties posed by big data and focus on effective, privacypreserving computation in the big data era. In order to address the effectiveness and privacy needs of Data Mining (DM) in the big data era, it first formalizes the overall framework of big data analytics, identifies the related privacy requirements, and introduces an effective and Privacy-Triple-DES as an instance.…”

Section: Introductionmentioning

confidence: 99%

Improving Privacy Preservation Approach for Healthcare Data using Frequency Distribution of Delicate Information

Puri¹,

Haritha²

2022

IJACSA

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In the modern world, everyone wishes that their personal information wouldn't be made public in any manner. In order to keep personal information hidden from prying eyes, privacy protection is essential. The data may be in the form of big data and minimization of risk and protection of sensitive data is important. In this research, a revolutionary customized privacy-preserving method is implemented that addresses the drawbacks of earlier personalized privacy as well as other anonymization methods. There are two main components that make up the proposed method's core. Delicate Information and Delicate Weight are two additional attributes which are used in the record table, are covered in the first section. The record holder's Delicate Information (DI) decides whether or not secrecy should be kept or if it should be shared. How delicate an attribute value is compared to the rest is indicated by its Delicate weight (DW). The second part covers a new representation used for anonymization termed the Frequency Distribution Block (FDB) and Quasi-Identifier Distribution Block (QIDB). According to experimental findings, the proposed system executes more quickly and with less data loss than current approaches.

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