CAREER: Privacy Analytics for Users in a Big Data World

职业：大数据世界中用户的隐私分析

• 批准号: 1253418
• 负责人: Rachel Greenstadt
• 金额: $ 29.29万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253418&HistoricalAwards=false
• 关键词: CAREER; Privacy; Analytics; Users; Big

Increasing amounts of data are being collected about users, and increasingly sophisticated analytics are being applied to this data for various purposes. Privacy analytics are machine learning and data mining algorithms applied by end-users to their data for the purpose of helping them manage both private information and their self-presentation. This research develops privacy analytics that help users answer three interconnected questions about their online persona (1) What data does the user consider sensitive, and in what contexts should one share it?; (2) What does the data say about the user; and (3) Who knows what? These privacy analytics introduce a novel, inverse data mining problem where users analyze their data to estimate the conclusions the data will produce when incorporated into larger data sets. This project designs new algorithms for quantitative and automated methods to detect privacy-related phenomena that have been observed qualitatively. These algorithms support the development of usable privacy enhancing technologies and will give users tools to cope with and manage their data in a complicated data environment. These tools will provide awareness to users about how their data is being used. These analytics will also help answer questions critical to the development of privacy law and policy.This work involves approximately twenty-five undergraduates in research activities, exposing them to research methods and privacy issues. This project also develops novel educational materials including course offerings for an interdisciplinary master's program in security and educational tools for use by the general public to bridge the digital divide.

越来越多的用户数据被收集，并且越来越复杂的分析被应用于这些数据以用于各种目的。隐私分析是最终用户对其数据应用的机器学习和数据挖掘算法，旨在帮助他们管理私人信息和自我呈现。 这项研究开发了隐私分析，帮助用户回答有关其在线角色的三个相互关联的问题（1）用户认为哪些数据敏感，以及在什么情况下应该共享这些数据？ (2) 数据说明了有关用户的什么信息； (3) 谁知道什么？这些隐私分析引入了一种新颖的逆数据挖掘问题，用户分析他们的数据以估计数据在合并到更大的数据集中时将产生的结论。 该项目设计了用于定量和自动化方法的新算法，以检测已定性观察到的与隐私相关的现象。这些算法支持可用隐私增强技术的开发，并将为用户提供在复杂数据环境中处理和管理数据的工具。这些工具将使用户了解他们的数据如何被使用。这些分析还将有助于回答对隐私法和政策的制定至关重要的问题。这项工作涉及大约 25 名本科生的研究活动，让他们接触研究方法和隐私问题。 该项目还开发新颖的教育材料，包括安全跨学科硕士课程的课程以及供公众使用的弥合数字鸿沟的教育工具。