面向数据权利、数据定价和隐私计算的数据驱动学习

Data-Driven Learning for Data Rights,Data Pricing,and Privacy Computing

近年来,数据已成为数字经济中最重要的生产要素之一。与传统生产要素不同,数据的数字化性质使其难以合同和交易。因此,建立一个高效和标准的数据交易市场体系将有利于降低成本,提高行业各方的生产力。尽管许多研究致力于数据法规和其他数据交易问题,如隐私和定价,但很少有工作对机器学习和数据科学领域的这些研究进行全面回顾。为了提供对这个主题的完整和最新的理解,本文涵盖了数据交易过程中的三个关键问题:数据权利、数据定价和隐私计算。通过厘清这些主题之间的关系,本文提供了一个数据生态系统的全貌,其中数据由个人、研究机构和政府等数据主体生成,而数据处理者出于创新或运营目的获取数据,并通过适当的定价机制根据数据主体各自的所有权分配收益。为了使人工智能(AI)能够长期有益于人类社会的发展,人工智能算法需要通过数据保护法规(即隐私保护法规)进行评估,以帮助构建日常生活中值得信赖的人工智能系统。

In recent years,data has become one of the most important resources in the digital economy.Unlike traditional resources,the digital nature of data makes it difficult to value and contract.Therefore,establishing an efficient and standard data-transaction market system would be beneficial for lowering cost and improving productivity among the parties in this industry.Although numerous studies have been dedicated to the issue of complying with data regulations and other data-transaction issues such as privacy and pricing,little work has been done to provide a comprehensive review of these studies in the fields of machine learning and data science.To provide a complete and up-to-date understanding of this topic,this review covers the three key issues of data transaction:data rights,data pricing,and privacy computing.By connecting these topics,this paper provides a big picture of a data ecosystem in which data is generated by data subjects such as individuals,research agencies,and governments,while data processors acquire data for innovational or operational purposes,and benefits are allocated according to the data’s respective ownership via an appropriate price.With the long-term goal of making artificial intelligence(AI)beneficial to human society,AI algorithms will then be assessed by data protection regulations(i.e.,privacy protection regulations)to help build trustworthy AI systems for daily life.