On the public citation graph
Preserving Patient Privacy When Sharing Same-Disease Data
0 signed · 35 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0378-3758(82)90058-1openalex-snapshot
Data-swapping: A technique for disclosure control
inferredcites →https://doi.org/10.1016/s0895-4356(01)00397-3openalex-snapshot
Using the national registry of HIV-infected veterans in research Lessons for the development of disease registries
inferredcites →https://doi.org/10.1038/srep01376openalex-snapshot
Unique in the Crowd: The privacy bounds of human mobility
inferredcites →https://doi.org/10.1080/07350015.1989.10509729openalex-snapshot
The Risk of Disclosure for Microdata
inferredcites →https://doi.org/10.1109/icde.2006.1openalex-snapshot
L-diversity: privacy beyond k-anonymity
inferredcites →https://doi.org/10.1109/icde.2006.101openalex-snapshot
Mondrian Multidimensional K-Anonymity
inferredcites →https://doi.org/10.1109/icde.2007.367856openalex-snapshot
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
inferredcites →https://doi.org/10.1142/s0218488502001648openalex-snapshot
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
inferredcites →https://doi.org/10.1145/1179601.1179615openalex-snapshot
Revisiting the uniqueness of simple demographics in the US population
inferredcites →https://doi.org/10.1145/1515693.1515695openalex-snapshot
A Bayesian Approach for Estimating and Replacing Missing Categorical Data
inferredcites →https://doi.org/10.1145/1515693.1516680openalex-snapshot
Overview and Framework for Data and Information Quality Research
inferredcites →https://doi.org/10.1145/2629604openalex-snapshot
Challenges for privacy preservation in data integration
inferredcites →https://doi.org/10.1145/2755489openalex-snapshot
Data and Analytics Challenges for a Learning Healthcare System
inferredcites →https://doi.org/10.1145/3979.4017openalex-snapshot
A data distortion by probability distribution
inferredcites →https://doi.org/10.1145/76894.76895openalex-snapshot
Security-control methods for statistical databases: a comparative study
inferredcites →https://doi.org/10.1201/9781420000177.ch38openalex-snapshot
Standards for Privacy of Individually Identifiable Health Information
inferredcites →https://doi.org/10.1287/isre.1070.0112openalex-snapshot
Releasing Individually Identifiable Microdata with Privacy Protection Against Stochastic Threat: An Application to Health Information
inferredcites →https://doi.org/10.1287/isre.1100.0289openalex-snapshot
Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data
inferredcites →https://doi.org/10.1287/mnsc.1120.1584openalex-snapshot
Class-Restricted Clustering and Microperturbation for Data Privacy
inferredcites →https://doi.org/10.1287/opre.1090.0702openalex-snapshot
Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining
inferredcites →https://doi.org/10.2337/diacare.29.02.06.dc05-1512openalex-snapshot
Coffee, Caffeine, and Risk of Type 2 Diabetes
inferredcites →https://doi.org/10.25300/misq/2014/38.3.03openalex-snapshot
Digression and Value Concatenation to Enable Privacy-Preserving Regression1
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-70863-8_29openalex-snapshot
Toward a Metadata Framework for Sharing Sensitive and Closed Data: An Analysis of Data Sharing Agreement Attributes
inferred← citeshttps://doi.org/10.1007/978-981-15-5679-1_49openalex-snapshot
Ensuring Data Privacy Using Machine Learning for Responsible Data Science
inferred← citeshttps://doi.org/10.1016/j.scs.2018.04.014openalex-snapshot
Privacy preserving data by conceptualizing smart cities using MIDR-Angelization
inferred← citeshttps://doi.org/10.1061/(asce)la.1943-4170.0000526openalex-snapshot
Regulatory Framework around Data Governance and External Benchmarking
inferred← citeshttps://doi.org/10.1109/icccnt49239.2020.9225342openalex-snapshot
Integrating Machine Learning with Blockchain to Ensure Data Privacy
inferred← citeshttps://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00322openalex-snapshot
A Privacy Protection Method for Medical Health Data
inferred← citeshttps://doi.org/10.1145/3476071openalex-snapshot
Tensions and Mitigations: Understanding Concerns and Values around Smartphone Data Collection for Public Health Emergencies
inferred← citeshttps://doi.org/10.1145/3613904.3642245openalex-snapshot
HIV Client Perspectives on Digital Health in Malawi
inferred← citeshttps://doi.org/10.1146/annurev-biodatasci-080917-013416openalex-snapshot
Privacy Policy and Technology in Biomedical Data Science
inferred← citeshttps://doi.org/10.1186/s40413-018-0194-3openalex-snapshot
Geolocation with respect to personal privacy for the Allergy Diary app - a MASK study
inferred← citeshttps://doi.org/10.3389/fbinf.2022.984807openalex-snapshot
Algorithms to anonymize structured medical and healthcare data: A systematic review
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.