{"title":"从公共数据中学习个性化隐私偏好","authors":"Wen Wang, Beibei Li","doi":"10.1287/isre.2023.0318","DOIUrl":null,"url":null,"abstract":"In the era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"46 26","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning Personalized Privacy Preference from Public Data\",\"authors\":\"Wen Wang, Beibei Li\",\"doi\":\"10.1287/isre.2023.0318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.\",\"PeriodicalId\":5,\"journal\":{\"name\":\"ACS Applied Materials & Interfaces\",\"volume\":\"46 26\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Materials & Interfaces\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1287/isre.2023.0318\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1287/isre.2023.0318","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Learning Personalized Privacy Preference from Public Data
In the era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.