保护隐私的联邦学习架构,在边缘端点上实现数据所有权和可移植性

Patience Mpofu, Solomon Hopewell Kembo, Marlvern Chimbwanda, Saulo Jacques, Nevil Chitiyo, Kudakwashe Zvarevashe
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摘要

为应对2019冠状病毒病(COVID-19)限制导致的粮食供应紧张,该项目于2020年开发了自动化家庭鱼菜共生装置,以保证粮食自给自足。但是,自动化鱼菜共生解决方案没有完全符合保护家庭所有者数据的数据隐私和可移植性最佳做法。本研究的目的是在先前开发的自动化鱼菜共生装置的基础上开发一个数据隐私和可移植性层。设计/方法论/方法设计科学研究(DSR)是本研究中实施的研究方法。通用数据保护和隐私法规(GDPR)启发的原则授权数据主体,包括数据最小化,目的限制,存储限制以及完整性和机密性,可以在使用Pinecone Matrix家庭服务器和边缘设备的联邦学习(FL)架构中实现。研究局限性/意义本研究回顾的文献表明,GDPR对数据可移植性的权利可以通过赋予个人对自己数据的更多控制权来对数据保护产生积极影响。这是通过允许数据主体以易于在另一上下文中重用的格式从数据控制者处获取其个人信息,并将该信息自由地传输给其选择的任何其他数据控制者来实现的。在发展中国家,数据可移植性没有受到数据保护法的严格管理或执行,例如津巴布韦的《2021年数据保护法》。实际意义隐私要求可以在终端技术中实施,例如智能手机,微控制器和单板计算机集群,使数据主体能够受到激励,同时在促进数据控制器和处理器之间竞争的过程中释放自己数据的价值。在边缘端点和雾服务器上使用Matrix Pinecone的端到端加密,以及数据可移植性的实际实现,目前在文献中没有充分覆盖。这项研究为未来关于这个话题的讨论提供了一个跳板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A privacy-preserving federated learning architecture implementing data ownership and portability on edge end-points
PurposeIn response to food supply constraints resulting from coronavirus disease 2019 (COVID-19) restrictions, in the year 2020, the project developed automated household Aquaponics units to guarantee food self-sufficiency. However, the automated aquaponics solution did not fully comply with data privacy and portability best practices to protect the data of household owners. The purpose of this study is to develop a data privacy and portability layer on top of the previously developed automated Aquaponics units.Design/methodology/approachDesign Science Research (DSR) is the research method implemented in this study.FindingsGeneral Data Protection and Privacy Regulations (GDPR)-inspired principles empowering data subjects including data minimisation, purpose limitation, storage limitation as well as integrity and confidentiality can be implemented in a federated learning (FL) architecture using Pinecone Matrix home servers and edge devices.Research limitations/implicationsThe literature reviewed for this study demonstrates that the GDPR right to data portability can have a positive impact on data protection by giving individuals more control over their own data. This is achieved by allowing data subjects to obtain their personal information from a data controller in a format that makes it simple to reuse it in another context and to transmit this information freely to any other data controller of their choice. Data portability is not strictly governed or enforced by data protection laws in the developing world, such as Zimbabwe's Data Protection Act of 2021.Practical implicationsPrivacy requirements can be implemented in end-point technology such as smartphones, microcontrollers and single board computer clusters enabling data subjects to be incentivised whilst unlocking the value of their own data in the process fostering competition among data controllers and processors.Originality/valueThe use of end-to-end encryption with Matrix Pinecone on edge endpoints and fog servers, as well as the practical implementation of data portability, are currently not adequately covered in the literature. The study acts as a springboard for a future conversation on the topic.
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