De-identification Techniques Research Articles

Privacy Preservation in the Age of Big Data

Anonymization or de-identification techniques are methods for protecting the privacy of subjects in sensitive data sets while preserving the utility of those data sets. The efficacy of these methods has come under repeated attacks as the ability to analyze large data sets becomes easier. Several researchers have shown that anonymized data can be reidentified to reveal the identity of the data subjects via approaches such as so-called “linking.” In this report, we survey the anonymization landscape of approaches for addressing re-identification and we identify the challenges that still must be addressed to ensure the minimization of privacy violations. We also review several regulatory policies for disclosure of private data and tools to execute these policies.

Awareness Survey of Anonymisation of Protected Health Information in Pakistan

With the growing advancement of science and technology, research has become the vital step in every educational field. This research survey sheds light on the methods of de-identification and anonymisation for protecting the privacy of the patients, practitioners and nurses. Researchers require huge amounts of patient data for carrying out different analyses. Patient information must therefore be preserved while ensuring that the applied privacy policies do not render the data less valuable. De-identification and anonymisation techniques masks the patient identity through various methods such as suppression, randomisation, shuffling, creating pseudonyms, generalisation, adding noise, scrambling, masking, encoding and encryption, etc. The dataset having critical information is called protected health information (PHI) through which an individual can be identified. Thus, PHI must be preserved through an appropriate means to make data valuable and at the same time, protect the data from hackers. This paper presents the importance of securing PHIs in Pakistan by analysing the results of an awareness survey.

Research of Specific Domestic De-identification Technique for Protection of Personal Health Medical Information in Review & Analysis of Overseas and Domestic De-Identification Technique

As life in a rapidly changing Internet age at home and abroad, large amounts of information are being used medical, financial, services, etc. Accordingly, especially hospitals, is an invasion of privacy caused by leakage and intrusion of personal information in the system in medical institutions, including clinics institutions. To protect the privacy & information protection of personal health medical information in medical institutions at home and abroad presented by national policies and de-identification processing technology standards in accordance with the legislation. By comparative analysis in existing domestic and foreign institutional privacy and de-identification technique, derive a advanced one of pseudonymization and anonymization techniques for destination data items that fell short in comparison to the domestic laws and regulations, etc. De-identification processing technology for personal health information is compared to a foreign country pharmaceutical situations. We propose a new de-identification techniques by reducing the risk of re-identification processing to enable the secondary use of domestic medical privacy.

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

Redefining genomic privacy: trust and empowerment.

Fulfilling the promise of the genetic revolution requires the analysis of large datasets containing information from thousands to millions of participants. However, sharing human genomic data requires protecting subjects from potential harm. Current models rely on de-identification techniques in which privacy versus data utility becomes a zero-sum game. Instead, we propose the use of trust-enabling techniques to create a solution in which researchers and participants both win. To do so we introduce three principles that facilitate trust in genetic research and outline one possible framework built upon those principles. Our hope is that such trust-centric frameworks provide a sustainable solution that reconciles genetic privacy with data sharing and facilitates genetic research.

Biobanking in Pediatrics: The Human Nonsubjects Approach

Pediatric biobanks are an indispensible resource for the research that will be needed to bring advances in personalized medicine into pediatric medical care. Investigators developing pediatric biobanks have struggled with the ethical and legal challenges that arise in pediatric research. This article explores how one biobank model, the 'human nonsubjects models', is able to respond to such common challenges as the role of the parent and the child in agreeing to research participation, reconsent at the age of majority, data sharing and return of research results. Although this approach does not involve formal informed consent, it is well-suited to pediatric biobanking owing to its potential to reduce risk to children through a combination of advanced deidentification techniques and extensive oversight.

An Overview of Techniques for De-Identifying Personal Health Information

There is increasing demand for the use and disclosure of personal health information (PHI) for secondary purposes. In the context of health information, 'secondary purposes' is defined as any retrospective processing of existing data that is not part of providing care to the patient. For example, data used or disclosed for analysis, research, safety and quality measurement and improvement, public health, payment, provider certification and accreditation, and marketing are considered secondary purposes under this definition [1]. This report describes, at a high level, the techniques that can be used to de-identify PHI. In particular, our objective is to make clear when specific de-identification techniques are applicable and how to decide when to use each.