Journal of Privacy and Confidentiality最新文献

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results 图上分析和机器学习的差异化私有保证：成果概览

Journal of Privacy and Confidentiality Pub Date : 2024-02-11 DOI: 10.29012/jpc.820

Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, R. Braren, D. Rueckert, Georgios Kaissis

{"title":"Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results","authors":"Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, R. Braren, D. Rueckert, Georgios Kaissis","doi":"10.29012/jpc.820","DOIUrl":"https://doi.org/10.29012/jpc.820","url":null,"abstract":"We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.","PeriodicalId":52360,"journal":{"name":"Journal of Privacy and Confidentiality","volume":"20 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139845759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results 图上分析和机器学习的差异化私有保证：成果概览

Journal of Privacy and Confidentiality Pub Date : 2024-02-11 DOI: 10.29012/jpc.820

Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, R. Braren, D. Rueckert, Georgios Kaissis

{"title":"Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results","authors":"Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, R. Braren, D. Rueckert, Georgios Kaissis","doi":"10.29012/jpc.820","DOIUrl":"https://doi.org/10.29012/jpc.820","url":null,"abstract":"We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.","PeriodicalId":52360,"journal":{"name":"Journal of Privacy and Confidentiality","volume":"118 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Beyond Legal Frameworks and Security Controls For Accessing Confidential Survey Data: Engaging Data Users in Data Protection 超越访问机密调查数据的法律框架和安全控制：让数据用户参与数据保护

Journal of Privacy and Confidentiality Pub Date : 2023-12-06 DOI: 10.29012/jpc.845

Amy Pienta, J. Jang, Margaret Levenstein

{"title":"Beyond Legal Frameworks and Security Controls For Accessing Confidential Survey Data: Engaging Data Users in Data Protection","authors":"Amy Pienta, J. Jang, Margaret Levenstein","doi":"10.29012/jpc.845","DOIUrl":"https://doi.org/10.29012/jpc.845","url":null,"abstract":"With a growing demand for data reuse and open data within the scientific ecosystem, protecting the confidentiality and privacy of survey data is increasingly important. It requires more than legal procedures and technological controls; it requires social and behavioral intervention. In this research note, we delineate the disclosure risks of various types of survey data (i.e., longitudinal data, social network data, sensitive information and biomarkers, and geographic data), the current motivation for data reuse and challenges to data protection. Despite rigorous efforts to protect data, there are still threats to mitigate the protection of confidentiality in microdata. Unintentional data breaches, protocol violations, and the misuse of data are observed even in well-established restricted data access systems, which indicates that the systems all may rely heavily on trust. Creating and maintaining that trust is critical to secure data access. We suggest four ways of building trust; User-Centered Design Practices; Promoting Trust for Protecting Confidential Data; General Training in Research Ethics; Specific Training in Data Security Protocols, with an example of a new project ‘Researcher Passport’ by the Inter-university Consortium for Political and Social Research. Continuous user-focused improvements in restricted data access systems are necessary so that we promote a culture of trust among the research and data user community, train both in the general topic of responsible research and in the specific requirements of these systems, and offer systematic and holistic solutions.","PeriodicalId":52360,"journal":{"name":"Journal of Privacy and Confidentiality","volume":"67 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138595982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protecting Sensitive Data Early in the Research Data Lifecycle 在研究数据生命周期的早期保护敏感数据

Journal of Privacy and Confidentiality Pub Date : 2023-12-06 DOI: 10.29012/jpc.846

Sebastian Karcher, Sefa Secen, Nicholas Weber

引用次数: 0

Restricted data management: the current practice and the future 受限数据管理：当前实践与未来

Journal of Privacy and Confidentiality Pub Date : 2023-12-06 DOI: 10.29012/jpc.844

J. Jang, Amy Pienta, Margaret Levenstein, Joe Saul

引用次数: 0

"I need a better description": An Investigation Into User Expectations For Differential Privacy “我需要一个更好的描述”:对用户对差异隐私期望的调查

Journal of Privacy and Confidentiality Pub Date : 2023-08-31 DOI: 10.29012/jpc.813

Rachel Cummings, Gabriel Kaptchuk, Elissa Redmiles

{"title":"\"I need a better description\": An Investigation Into User Expectations For Differential Privacy","authors":"Rachel Cummings, Gabriel Kaptchuk, Elissa Redmiles","doi":"10.29012/jpc.813","DOIUrl":"https://doi.org/10.29012/jpc.813","url":null,"abstract":"Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differentially private systems. Further, we attempt to understand (3) users' privacy expectations of the differentially private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424).  We find that users care about the kinds of information leaks against which differential privacy protects and are more willing to share their private information when the risks of these leaks are less likely to happen. Additionally, we find that the ways in which differential privacy is described in-the-wild haphazardly set users' privacy expectations, which can be misleading depending on the deployment. We synthesize our results into a framework for understanding a user's willingness to share information with differentially private systems, which takes into account the interaction between the user's prior privacy concerns and how differential privacy is described.","PeriodicalId":52360,"journal":{"name":"Journal of Privacy and Confidentiality","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135947024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesizing Familial Linkages for Privacy in Microdata 微数据中隐私的家族关联综合

Journal of Privacy and Confidentiality Pub Date : 2023-08-31 DOI: 10.29012/jpc.767

Gary Benedetto, Evan Totty

引用次数: 1

Privacy-Preserving Data Sharing for Genome-Wide Association Studies. 全基因组关联研究的隐私保护数据共享。

Journal of Privacy and Confidentiality Pub Date : 2013-01-01

Caroline Uhlerop, Aleksandra Slavković, Stephen E Fienberg

引用次数: 0