EAGER: Understanding and Categorizing Metadata

EAGER：理解元数据并对其进行分类

• 批准号: 1923528
• 负责人: Susan Landau
• 金额: $ 29.02万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923528&HistoricalAwards=false
• 关键词: EAGER; Understanding; Categorizing; Metadata

Communications metadata is necessary for the delivery of services. But such metadata leaks information about user intent and behavior. Through timing, for example, one can determine whether a Twitter account is a bot, while through packet length, what language is being spoken in an encrypted Voice Over Internet Protocol (VoIP) call. Thus, protecting a user's privacy and security is complicated. By examining multiple different sets of metadata usage, this EAGER seeks to develop a categorization of the types of information that metadata reveals. Applications of the work should lead to the development of better technical and policy protections of user privacy and security. The main contribution of this proposed research is a categorization of the types of uses to which metadata is applied. There are many uses in metadata usage (how is the data collected, is it aggregated with other data, etc.); this categorization will focus on developing clean types of information revealed through metadata capture and use. Such a categorization of metadata usage will have several important applications. Delineating different types of knowledge gained through the use of metadata (e.g., information about the device, information about the user), will simplify providing better technical protections for user privacy and security against such attacks. Furthermore, by categorizing the different ways metadata is used, it will be possible to more easily understand which usages are privacy invasive and which are not, and thus to develop more privacy-protective policies surrounding metadata usage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通信元数据是提供服务所必需的。但这样的元数据会泄露有关用户意图和行为的信息。例如，通过计时，可以确定Twitter帐户是否是机器人，而通过数据包长度，可以确定加密的互联网语音协议（VoIP）呼叫中使用的语言。因此，保护用户的隐私和安全是复杂的。通过检查多个不同的元数据使用集，EAGER试图对元数据揭示的信息类型进行分类。 这项工作的应用应该导致开发更好的技术和政策保护用户隐私和安全。这项研究的主要贡献是对元数据应用的类型进行了分类。元数据的使用有很多用途（数据是如何收集的，它是否与其他数据聚合，等等）;这一分类将侧重于开发通过元数据获取和使用而揭示的干净类型的信息。 元数据使用的这种分类将有几个重要的应用。描述通过使用元数据获得的不同类型的知识（例如，关于设备的信息、关于用户的信息），将简化为用户隐私和安全提供更好的技术保护以防止这种攻击。此外，通过对元数据的不同使用方式进行分类，可以更容易地了解哪些使用是侵犯隐私的，哪些不是，从而围绕元数据的使用制定更多的隐私保护政策。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Reversing Privacy Risks: Strict Limitations on the Use of Communications Metadata and Telemetry Information

扭转隐私风险：严格限制通信元数据和遥测信息的使用

• 发表时间: 2023
• 期刊: Colorado technology law journal
• 作者: Landau, Susan;Vargas Leon, Patricia
• 通讯作者: Vargas Leon, Patricia

Differential Privacy and Swapping: Examining De-Identification’s Impact on Minority Representation and Privacy Preservation in the U.S. Census

差异化隐私和交换：检查去识别化对美国人口普查中少数群体代表性和隐私保护的影响

• 发表时间: 2022
• 期刊: 2022 IEEE Symposium on Security and Priavcy
• 作者: Christ, Miranda;Radway, Sarah;Bellovin, Steven M.
• 通讯作者: Bellovin, Steven M.