Leakage in data mining

Kaufman, Shachar; Rosset, Saharon; Perlich, Claudia

doi:10.1145/2020408.2020496

Cited by 97 publications

(41 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in a top medical data mining contest, the accession numbers revealed the disease status of the patient, which was the aim of the contest 57 . In addition, pattern analysis of a large amount of public data revealed temporal and spatial commonalities in the assignment system that allowed predictions of US social security numbers (SSNs) from quasi-identifiers 58 .…”

Section: Identity Tracing Attacksmentioning

confidence: 99%

Routes for breaching and protecting genetic privacy

2014

View full text Add to dashboard Cite

We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these datasets is vital for progress in biomedical research. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, point to the underlying assumptions, and assess its technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.

show abstract

Section: Identity Tracing Attacksmentioning

confidence: 99%

Routes for breaching and protecting genetic privacy

2014

View full text Add to dashboard Cite

show abstract

“…In some contexts, it may not be possible to correct for all of these differences to the degree that a deep network is unable to use them. Moreover, the complexity of deep networks makes it difficult to determine when their predictions are likely to be based on such nominally irrelevant features of the data (called ‘leakage’ in other fields [ 167 ]). When we are not careful with our data and models, we may inadvertently say more about the way the data were collected (which may involve a history of unequal access and discrimination) than about anything of scientific or predictive value.…”

Section: Deep Learning and Patient Categorizationmentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2018

J. R. Soc. Interface.

1,657

1,001

View full text Add to dashboard Cite

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

show abstract

“…Raw microarray and RNA-seq data were log 2 transformed where necessary after which the data was (inner) merged and randomly divided into a training (2/3) and test set (1/3). As the test set should remain hidden from the training set, raw microarray and RNA-seq data was used instead of normalized data to prevent data leakage (56). Elastic net regression was subsequently performed using the R glmnet (v2.0) (57) package where we set the alpha to 0.8.…”

Section: Classification Analysismentioning

confidence: 99%