Privacy-Preserving Storage and Access of Medical Data through Pseudonymization and Encryption

Heurix, Johannes; Neubauer, Thomas

doi:10.1007/978-3-642-22890-2_16

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…Prior work proposed the assignment of identical pseudonyms to records linked to the same participant or patient in order to enable record grouping or patient-specific data joins [4,13]. Pseudonymization is often combined with encryption to ensure both deidentification and confidentiality of the data [13]. Pseudonyms establish linked records within the database during the whole lifecycle of the database.…”

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study

2019

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Background Clinical and social trials create evidence that enables medical progress. However, the gathering of personal and patient data requires high security and privacy standards. Direct linking of personal information and medical data is commonly hidden through pseudonymization. While this makes unauthorized access to personal medical data more difficult, a centralized pseudonymization list can still pose a security risk. In addition, medical data linked via pseudonyms can still be used for data-driven reidentification. Objective Our objective was to propose a novel approach to pseudonymization based on public-private key cryptography that allows (1) decentralized patient-driven creation and maintenance of pseudonyms, (2) 1-time pseudonymization of each data record, and (3) grouping of patient data records even without knowing the pseudonymization key. Methods Based on public-private key cryptography, we set up a signing mechanism for patient data records and detailed the workflows for (1) user registration, (2) user log-in, (3) record storing, and (4) record grouping. We evaluated the proposed mechanism for performance, examined the potential risks based on cryptographic collision, and carried out a threat analysis. Results The performance analysis showed that all workflows could be performed with an average runtime of 0.057 to 42.320 ms (user registration), 0.083 to 0.606 ms (record creation), and 0.005 to 0.198 ms (record grouping) depending on the chosen cryptographic tools. We expected no realistic risk of cryptographic collision in the proposed system, and the threat analysis revealed that 3 distinct server systems of the proposed setup had to be compromised to allow access to combined medical data and private data. However, this would still allow only for data-driven deidentification. For a full reidentification, all 3 trial servers and all study participants would have to be compromised. In addition, the approach supports consent management, automatically anonymizes the data after trial closure, and provides basic mechanisms against data forging. Conclusions The proposed approach has a high security and privacy level in comparison with traditional centralized pseudonymization approaches and does not require a trusted third party. The only drawback in comparison with central pseudonymization is the directed feedback of accidental findings to individual participants, as this is not possible with a quasi-anonymous storage of patient data.

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Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study

2019

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“…The pesudonymization is the earliest technique used for privacy-preserving eHealthcare system. Many protocols around the pesudonymization [21]- [25] have been studied in recent years. The key idea is to remove all the information that can identify the users.…”

Section: A Pseudonymization Based Schemes For Ehealthcare Systemmentioning

confidence: 99%

Achieve Privacy-Preserving Priority Classification on Patient Health Data in Remote eHealthcare System

Wang

Guan

2019

IEEE Access

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The wireless body area network (WBAN) has attracted considerable attention and becomes a promising approach to provide a 24-h on-the-go healthcare service for users. However, it still faces many challenges on the privacy of users' sensitive personal information and the confidentiality of healthcare center's disease models. For this reason, many privacy-preserving schemes have been proposed in recent years. However, the efficiency and accuracy of those privacy-preserving schemes become a big issue to be solved. In this paper, we propose an efficient and privacy-preserving priority classification scheme, named PPC, for classifying patients' encrypted data at the WBAN-gateway in a remote eHealthcare system. Specifically, to reduce the system latency, we design a non-interactive privacy-preserving priority classification algorithm, which allows the WBAN-gateway to conduct the privacy-preserving priority classification for the received users' medical packets by itself and to relay these packets according to their priorities (criticalities). A detailed security analysis shows that the PPC scheme can achieve the priority classification and packets relay without disclosing the privacy of the users' personal information and the confidentiality of the healthcare center's disease models. In addition, the extensive experiments with an android app and two java server programs demonstrate its efficiency in terms of computational costs and communication overheads.

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“…Giving the patients the control of how to encrypt their files and whom to give access to what part of their PHR is a novel approach. Heurix et al in [13] discuss the prejudice and discrimination patients are subjected to in the event of unauthorized disclosure of their Electronic Health Records (EHR) to insurance companies or even potential employers. The authors present a pseudonymization approach that preserves the patient's privacy and data confidentiality and provides a balance between privacy and data usability.…”

Section: Healthcarementioning

confidence: 99%

Privacy in the age of Pervasive Internet and Big Data Analytics – Challenges and Opportunities

Punagin¹,

Arya²

2015

IJMECS

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In the age of pervasive internet where people are communicating, networking, buying, paying bills, managing their health and finances over the internet, where sensors and machines are tracking real-time information and communicating with each other, it is but natural that big data will be generated and analyzed for the purpose of "smart business" and "personalization". Today storage is no longer a bottleneck and the benefit of analysis outweighs the cost of making user profiling omnipresent. However, this brings with it several privacy challengesrisk of privacy disclosure without consent, unsolicited advertising, unwanted exposure of sensitive information and unwarranted attention by malicious interests. We survey privacy risks associated with personalization in Web Search, Social Networking, Healthcare, Mobility, Wearable Technology and Internet of Things. The article reviews current privacy challenges, existing privacy preserving solutions and their limitations. We conclude with a discussion on future work in user controlled privacy preservation and selective personalization, particularly in the domain of search engines.

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Privacy-Preserving Storage and Access of Medical Data through Pseudonymization and Encryption

Cited by 14 publications

References 6 publications

Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study

Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study

Achieve Privacy-Preserving Priority Classification on Patient Health Data in Remote eHealthcare System

Privacy in the age of Pervasive Internet and Big Data Analytics – Challenges and Opportunities

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