<b>Research Note</b>—Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation

Melville, Nigel P.; McQuaid, Michael

doi:10.1287/isre.1110.0361

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Bootstrapping has been previously used in statistical disclosure limitation. Fienberg (), Fienberg, Makov, and Steele (), Raghunathan, Reiter, and Rubin (), and Melville and McQuaid (), discussed the use of bootstrap‐style resampling for selecting microdata records to be released to the public. Ichim () proposed the use of a quantile‐based bootstrap for generating synthetic data.…”

Section: Existing Methods For Disclosure Prevention In Rasmentioning

confidence: 99%

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

2015

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National Statistical Agencies and other data custodian agencies hold a wealth of data regarding individuals and organizations, collected from censuses, surveys and administrative sources. In many cases, these data are made available to external researchers, for the investigation of questions of social and economic importance. To enhance access to this information, several national statistical agencies are developing remote analysis systems (RAS) designed to accept queries from a researcher, run them on data held in a secure environment, and then return the results. RAS prevent a researcher from accessing the underlying data, and most rely on manual checking to ensure the responses have acceptably low disclosure risk. However, the need for scalability and consistency will increasingly require automated methods. We propose a RAS output confidentialization procedure based on statistical bootstrapping that automates disclosure control while achieving a provably good balance between disclosure risk and usefulness of the responses. The bootstrap masking mechanism is easy to implement for most statistical queries, yet the characteristics of the bootstrap distribution assure us that it is also effective in providing both useful responses and low disclosure risk. Interestingly, our proposed bootstrap masking mechanism represents an ideal application of Efron's bootstrap-one that takes advantage of all the theoretical properties of the bootstrap, without ever having to construct the bootstrap distribution.

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Section: Existing Methods For Disclosure Prevention In Rasmentioning

confidence: 99%

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

2015

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“…Generalization, suppression, and swapping apply to both categorical and numeric data. Noise perturbation adds noise to the original data to disguise their true values (Agrawal and Srikant 2000, Li and Sarkar 2013, Melville and McQuaid 2012), which applies mainly to numeric data. There are also studies that address privacy disclosure problems by hiding sensitive information (Menon et al 2005).…”

Section: Related Workmentioning

confidence: 99%

“…Applications and analyses based on structured data typically include traditional statistical analysis such as regression and multivariate analysis and data mining applications such as classification and clustering. Anonymization techniques are developed for these applications accordingly (Agrawal and Srikant 2000, Aggarwal and Yu 2008, Fung et al 2010, Li and Sarkar 2011, Melville and McQuaid 2012). For unstructured data such as medical text data, applications often include text mining tasks such as medical keyword-based search query and information extraction, as well as those that are also suitable for structured or semistructured data such as counting query and association analysis (Jensen et al 2012, Meystre et al 2008, Murphy et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Anonymizing and Sharing Medical Text Records

Qin

2017

Information Systems Research

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Health information technology has increased accessibility of health and medical data and benefited medical research and healthcare management. However, there are rising concerns about patient privacy in sharing medical and healthcare data. A large amount of these data are in free text form. Existing techniques for privacy-preserving data sharing deal largely with structured data. Current privacy approaches for medical text data focus on detection and removal of patient identifiers from the data, which may be inadequate for protecting privacy or preserving data quality. We propose a new systematic approach to extract, cluster, and anonymize medical text records. Our approach integrates methods developed in both data privacy and health informatics fields. The key novel elements of our approach include a recursive partitioning method to cluster medical text records based on the similarity of the health and medical information and a value-enumeration method to anonymize potentially identifying information in the text data. An experimental study is conducted using real-world medical documents. The results of the experiments demonstrate the effectiveness of the proposed approach.

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“…Hence, AI is commonly considered an interdisciplinary research area that attracts considerable attention both in economics and social domains as it offers a myriad of technological breakthroughs with regard to systems security [2]. There is a universal trend of investing in AI technology to face security challenges of our daily lives, such as statistical data, medicine, and transportation [3]. Some claim that specific data from key sectors have supported the development of AI, namely the availability of data from e-commerce [4], businesses [5], and government [6], which provided substantial input to ameliorate diverse machine-learning solutions and algorithms, in particular with respect to systems security [7].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Raimundo

Rosário

2021

Sensors

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Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.

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Research Note—Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation

Cited by 13 publications

References 37 publications

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

Anonymizing and Sharing Medical Text Records

The Impact of Artificial Intelligence on Data System Security: A Literature Review

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