Privacy Changes Everything

Rogers, Jennie; Bater, Johes; He, Xi; Machanavajjhala, Ashwin; Suresh, M.; Wang, Xiao

doi:10.1007/978-3-030-33752-0_7

Cited by 3 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their particular success lies in controlling for data source heterogeneity and maintaining high faithfulness to gold standard ( i.e., analysis of aggregated data). On the technical front, another anticipated development is that of trustworthy databases as described by Rogers et al, in which complementary scenarios of trusted database/untrustworthy analyst and untrustworthy cloud/trusted analyst motivate the technical requirements 101 .…”

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

show abstract

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

Purpose Scan: A Purpose-Aware Access Method

Praciano¹,

Amora²,

Abreu³

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

Yoon

Stanley

Christian

et al. 2022

CBM

View full text Add to dashboard Cite

BACKGROUND: With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. OBJECTIVE: The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. METHODS: The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. RESULTS: The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

show abstract

Privacy Changes Everything

Cited by 3 publications

References 48 publications

Review of Clinical Research Informatics

Review of Clinical Research Informatics

Purpose Scan: A Purpose-Aware Access Method

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

Contact Info

Product

Resources

About