A framework to preserve the privacy of electronic health data streams

Kim, Soo-Hyung; Sung, Min Kyoung; Chung, Yon Dohn

doi:10.1016/j.jbi.2014.03.015

Cited by 57 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the counterfeiting approach, a record is attempted to be hidden within a group of records. By hiding each record within a group of fake records, we avoid the delay inherent to the previous approach Kim et al (2014). The main drawback is the overhead introduced by the addition of fake records.…”

Section: Data Stream Anonymisationmentioning

confidence: 99%

Privacy-Preserving Technologies

Domingo-Ferrer

Blanco-Justicia

2020

The International Library of Ethics, Law and Technology

View full text Add to dashboard Cite

This chapter introduces privacy and data protection by design, and reviews privacy-enhancing techniques (PETs). Although privacy by design includes both technical and operational measures, the chapter focuses on the technical measures. First, it enumerates design strategies. Next, it considers privacy-enhancing techniques that directly address the hide strategy, but also aid in implementing the separate, control and enforce strategies. Specifically, it addresses PETs for: (1) identification, authentication and anonymity; (2) private communications; (3) privacy-preserving computations; (4) privacy in databases; and (5) discrimination prevention in data mining.

show abstract

Section: Data Stream Anonymisationmentioning

confidence: 99%

Privacy-Preserving Technologies

Domingo-Ferrer

Blanco-Justicia

2020

The International Library of Ethics, Law and Technology

View full text Add to dashboard Cite

show abstract

“…He et al [14] proposed a graph-based anonymization algorithm to prevent equivalence attacks on incremental anonymized data. Noise addition [16,17] is the main idea of perturbation. In [15], a polynomial-time algorithm based on the (k, e)-anonymity model was designed to solve the problem of an incremental privacy breach.…”

Section: Of 18mentioning

confidence: 99%

Personalized extended (α, k)‐anonymity model for privacy‐preserving data publishing

Liu

Xie

Wang

2016

Concurrency and Computation

View full text Add to dashboard Cite

General (˛, k)-anonymity model is a widely used method in privacy-preserving data publishing, but it cannot provide personalized anonymity. At present, two main schemes for personalized anonymity are the individual-oriented anonymity and the sensitive value-oriented anonymity. Unfortunately, the existing personalized anonymity models, designed for any of the aforementioned schemes for privacy-preserving data publishing, are not effective enough to meet the personalized privacy preservation requirement. In this paper, we propose a novel personalized extended scheme to provide the personalized services in general (˛, k)-anonymity model. The sensitive value-oriented anonymity is combined with the individual-oriented anonymity in the new personalized extended (˛, k)-anonymity model by the following two steps: (1) The sensitive attribute values are divided into several groups according to their sensitivities, and each group is assigned with its own frequency constraint threshold. (2) A guarding node is set for each individual to replace his/her sensitive value if necessary. We implement the personalized extended (˛, k)-anonymity model with a clustering algorithm. The performance evaluation finally shows that our model can provide stronger privacy preservation efficiently as well as achieving the personalized service. k-anonymity model assumes that each particular individual corresponds to a record in a twodimensional data set. Each record contains three types of attributes: identifier, quasi-identifier (QI), and sensitive attribute. Identifier is removed first; QI values are replaced with values that lessspecific but semantically consistent. As a result, each record in the modified data set is identical to at least k -1 other records with respect to QI. Based on k-anonymity model, a variety of improved privacy principles, such as l-diversity model [2], t-closeness model [3], general (˛, k)-anonymity model [4], are put forth to model and quantify privacy.The traditional anonymization principles mentioned earlier, however, focus on a universal approach that exerts the same amount of preservation for all individuals and do not take the personalized privacy preservation requirement into account. Individuals may have many differences in the sensitivity of the same privacy information in real life, which would lead to different privacy preservation requirements. Therefore, it is necessary to provide the privacy preservation of personalized service. The notion of personalized anonymity [5] was first proposed by Xiao et al., and some personalized anonymization solutions were subsequently given out [6][7][8][9][10]. In general, these personalized anonymization principles include two kinds of schemes, individual-oriented personalized anonymity and sensitive value-oriented personalized anonymity. The former may result in the impropriety of some parameters relevant to privacy constraint if complying with the privacy preservation requirements of individuals only. The latter ignores the privacy preservation requirements of individu...

show abstract

“…Kim et al in [8] proposed a delay-free anonymization framework, to protect data against potential privacy breaches. Unlike existing work, l-diverse artificial sensitive data are generated and added to main sensitive data instead of quasiidentifier attribute be generalized.…”

Section: Related Workmentioning

confidence: 99%

Fast

Mohammadian

Noferesti

Jalili

2014

Proceedings of the 2014 International Conference on Big Data Science and Computing

View full text Add to dashboard Cite

This paper proposes an anonymization algorithm (FAST) to speed up anonymization of big data streams. The proposed parallel algorithm provides an efficient big data anonymization by a multithread technique. A proactive time-expiration heuristic is applied to publish data before they are being expired. Our simulation results indicate significant improvement in big data stream anonymization in terms of information loss and cost metric.

show abstract

A framework to preserve the privacy of electronic health data streams

Cited by 57 publications

References 12 publications

Privacy-Preserving Technologies

Privacy-Preserving Technologies

Personalized extended (α, k)‐anonymity model for privacy‐preserving data publishing

Fast

Contact Info

Product

Resources

About