Collaborative Research: DASS: A Socio-Technical Framework for Handling Digital Evidence with Security and Privacy Assurances

越来越多的数字设备在社会上的使用带来了越来越大的隐私风险。最重要的是，科技公司和执法部门对利用存储在我们设备上的海量数据的互补需求，正在使隐私性、自治性和匿名性的隐私利益复杂化。尽管数据隐私从技术和法律角度都得到了极大的关注，但这两个学术角度很少结合在一起来揭示这个对我们的社会日益重要的话题所固有的跨学科相关性和协同效应。利用数字证据收集的背景，本项目从法律和技术角度研究隐私和安全问题。在技术方面，调查人员研究如何以更有针对性的方式搜索数据，以及如何防止数据被未经授权更改。在法律方面，新的技术能力将推动相关主题的新学术研究。数字证据在刑事诉讼中的兴起引发了指导该项目的两个主要问题。首先，根据宪法第四修正案，法官经常将设备搜索限制在与调查相关的特定数据上，尽管各种类型的数据可能会混合在一起，这一点很复杂。其次，一旦执法部门收集了数字证据，他们就必须防止篡改，防止“内部”和“外部”黑客侵犯隐私。该项目通过以下贡献应对这两组挑战：1)开发对法律指令(包括关于数字搜索范围的基于授权和基于同意的限制)作出反应的软件，并使用基于元数据的分析和自然语言处理(NLP)技术的组合，仅从包含混合数据的设备中检索相关证据；2)解决自然语言指令中的歧义和自动分类的准确性限制，同时考虑侵犯隐私和进行未经授权的搜索的法律和社会后果；3)提供保护数据机密性和检测数据篡改的安全机制；4)使用区块链技术创建对执法部门存储的数据的所有访问和修改的不可变日志；以及5)进一步开发和应用正式验证技术，以解释所实施协议的安全性。除了加强数字取证，这项工作还适用于政府和行业的数据做法，特别是在处理异类数据以及在远远超出刑事诉讼的情况下分离和保护数据方面。该项目在课程开发方面影响了几个现有的和新的班级，横跨数字取证、安全和数据科学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The ever-increasing use of digital devices in society is creating mounting privacy risks. Above all, complementary desires from technology companies and law enforcement to harness the vast troves of data stored on our devices are complicating privacy interests in seclusion, autonomy, and anonymity. Though data privacy has received significant attention from both technological and legal angles, the two scholarly perspectives rarely combine to expose the interdisciplinary dependencies and synergies inherent in this topic of growing significance to our society. Using the context of digital evidence collection, this project studies legal and technical angles to privacy and security concerns. From the technical side, the investigators study how the data can be searched in a more targeted fashion and how data can be kept from unauthorized alteration. From the legal side, new technical capabilities will motivate new scholarship on related topics. The rise of digital evidence in criminal proceedings triggers two principal issues guiding this project. First, under the Fourth Amendment to the Constitution, judges often limit device searches to specific data relevant to the investigation, despite the complication that various types of data may be intermixed. Second, once law enforcement has collected digital evidence, they must guard against tampering and prevent privacy invasions by both “inside” and “outside” hackers. This project addresses these two sets of challenges through the following contributions: 1) Developing software that responds to legal directives (including warrant-based and consent-based restrictions governing the scope of digital searches) and retrieves only relevant evidence from a device containing intermingled data, using a combination of metadata-based analysis and natural language processing (NLP) techniques; 2) Addressing the ambiguity in natural language directives and the accuracy limitations of automatic classification, while considering legal and social consequences of violating privacy and conducting unauthorized searches; 3) Providing security mechanisms that preserve data confidentiality and detect data tampering; 4) Using blockchain technology to create immutable logs of all accesses to, and modification of, data stored by law enforcement; and 5) Further developing and applying formal verification techniques that reason about the security of the protocols implemented. In addition to enhancing digital forensics, the work is applicable to data practices in both government and industry, particularly in dealing with heterogeneous data and separating and securing data in contexts far beyond criminal proceedings. The project impacts several existing and new classes in terms of curriculum development, cutting across digital forensics, security, and data sciences.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.