The era of Big Data has brought with it a richer understanding of user behavior through massive data sets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can provide Municipal Authorities (MA) with insights on how to operate, regulate, or improve the transportation network. Mobility data, however, may contain sensitive information about end users and trade secrets of Mobility Providers (MP). Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose an interactive protocol between a MA and a MP in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users. This is accomplished in two steps: a commitment step, and a computation step. In the first step, Merkle commitments and aggregated traffic measurements are used to generate a cryptographic commitment. In the second step, MP extracts insights from the data and sends them to MA. Using the commitment and zero-knowledge proofs, MA can certify that the information received from MP is accurate, without needing to directly inspect the mobility data. We also present a differentially private version of the protocol that is suitable for the large query regime. The protocol is verifiable for both MA and MP in the sense that dishonesty from one party can be detected by the other. The protocol can be readily extended to the more general setting with multiple MPs via secure multi-party computation.
翻译:大数据时代带来了对用户行为的更深入理解,通过大量数据集,可以帮助各组织优化其服务质量。在运输研究方面,流动数据可以向市政当局提供关于如何操作、监管或改进运输网络的见解。但流动数据可能包含关于终端用户的敏感信息以及移动提供者的贸易秘密。由于数据隐私的关注,议员可能不愿意向移动管理局提供他们的数据集。利用密码学的理念,我们提议在MAA和MP之间建立互动协议,让MAA能够从流动数据中获得洞察力,而不必让MP透露其贸易秘密或用户敏感数据。这是分两个步骤完成的:承诺步骤和计算步骤。第一步,Merkle承诺和综合交通测量用于生成加密承诺。第二步,MP从数据中提取洞察力并将其发送给MA。利用承诺和零识别证据,MAA可以证明从MP得到的信息是准确的,而不需要直接检查流动数据。我们还可以通过两个步骤完成这项工作:承诺步骤,一个是计算步骤,默克尔承诺和综合交通测量方法,一个程序可以被安全地通过另一个程序,一个程序,一个程序可以安全地被安全地检测。