An Improved Data Sanitization Algorithm for Privacy Preserving Medical Data Publishing

Zaman, A. N. K.; Obimbo, Charlie; Dara, Rozita

doi:10.1007/978-3-319-57351-9_8

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…Because the sensitive data contained in medical data is not only the final label but also various sensitive attributes, the method in [44] requires a great limitation on the attackeraŕs background knowledge, and the security level is insufficient. However, not much is found in papers that address the privacy preservation to achieve the goal of classification [45], [54]. The content of [45] is the closest to our work, and the data are also medical data.…”

Section: Related Workmentioning

confidence: 91%

“…However, not much is found in papers that address the privacy preservation to achieve the goal of classification [45], [54]. The content of [45] is the closest to our work, and the data are also medical data. In [45], anonymity technology and differential privacy are combined to protect published medical data, which enhances the security of the published data.…”

Section: Related Workmentioning

confidence: 91%

“…The content of [45] is the closest to our work, and the data are also medical data. In [45], anonymity technology and differential privacy are combined to protect published medical data, which enhances the security of the published data. Then, the protected data is treated as the training data of the C4.5 model to observe the classification effect, and the classification accuracy is enhanced by increasing the taxonomy tree depth (TTD).…”

Section: Related Workmentioning

confidence: 97%

“…This method meets the needs of previous researchers and can provide strong privacy protection for data privacy. The research on differential privacy can generally be divided into two directions: one is data publishing under privacy protection [13], [18], [35]- [45]; the other is privacy protection for machine learning [29], [40], [46]- [54].…”

Section: Related Workmentioning

confidence: 99%

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Differential Privacy for Data and Model Publishing of Medical Data

et al. 2019

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Combining medical data and machine learning has fully utilized the value of medical data. However, medical data contain a large amount of sensitive information, and the inappropriate handling of data can lead to the leakage of personal privacy. Thus, both publishing data and training data in machine learning may reveal the privacy of patients. To address the above issue, we propose two effective approaches. One combines a differential privacy and decision tree (DPDT) approach to provide strong privacy guarantees for publishing data, which establishes a weight calculation system based on the classification and regression tree (CART) method and takes weights as a new element of differential privacy to participate in privacy protection and reduce the negative impact of differential privacy on data availability. Another uses the differentially private mini-batch gradient descent algorithm (DPMB) to provide strong protection for training data; it tracks the privacy loss and allows the model to satisfy differential privacy in the process of gradient descent to prevent attackers from invading personal privacy with the training data. It is worth mentioning that, in this paper, we adopt the data processed by DPDT as the training data of DPMB to further strengthen the privacy of data. INDEX TERMS Deep learning, data privacy, differential privacy, data publishing.

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Section: Related Workmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Differential Privacy for Data and Model Publishing of Medical Data

et al. 2019

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“…More complex and comprehensive privacy models have also received significant attention. In particular, the differential privacy model was investigated extensively [53,55,56,[58][59][60][61][62]. It is notable, that among these more modern approaches, a variety has focused on biomedical data [56,57,60].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models

Eicher

Bild

Spengler

et al. 2020

BMC Med Inform Decis Mak

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Background: Modern data driven medical research promises to provide new insights into the development and course of disease and to enable novel methods of clinical decision support. To realize this, machine learning models can be trained to make predictions from clinical, paraclinical and biomolecular data. In this process, privacy protection and regulatory requirements need careful consideration, as the resulting models may leak sensitive personal information. To counter this threat, a wide range of methods for integrating machine learning with formal methods of privacy protection have been proposed. However, there is a significant lack of practical tools to create and evaluate such privacy-preserving models. In this software article, we report on our ongoing efforts to bridge this gap. Results: We have extended the well-known ARX anonymization tool for biomedical data with machine learning techniques to support the creation of privacy-preserving prediction models. Our methods are particularly well suited for applications in biomedicine, as they preserve the truthfulness of data (e.g. no noise is added) and they are intuitive and relatively easy to explain to non-experts. Moreover, our implementation is highly versatile, as it supports binomial and multinomial target variables, different types of prediction models and a wide range of privacy protection techniques. All methods have been integrated into a sound framework that supports the creation, evaluation and refinement of models through intuitive graphical user interfaces. To demonstrate the broad applicability of our solution, we present three case studies in which we created and evaluated different types of privacy-preserving prediction models for breast cancer diagnosis, diagnosis of acute inflammation of the urinary system and prediction of the contraceptive method used by women. In this process, we also used a wide range of different privacy models (k-anonymity, differential privacy and a game-theoretic approach) as well as different data transformation techniques. Conclusions: With the tool presented in this article, accurate prediction models can be created that preserve the privacy of individuals represented in the training set in a variety of threat scenarios. Our implementation is available as open source software.

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