中圖分類號(hào)：D922.174
文獻(xiàn)標(biāo)識(shí)碼：A
DOI:10.19358/j.issn.2097-1788.2023.06.004
引用格式：孫綺雯.聯(lián)邦學(xué)習(xí)框架下的數(shù)據(jù)安全與利用合規(guī)路徑［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理,，2023,，42（6）：21-29.

Data security and utilization compliance path under the federated learning framework

Abstract： The increasingly stringent laws and regulations related to personal information protection have increased the difficulty and cost of compliance in data circulation of enterprises while protecting personal privacy. Under the framework of federated learning, the privacy protection design that does not transmit the original data but only transmits the model uses technology to promote legal compliance, which can be a possible solution for data fusion and collaborative innovation under the premise of breaking the barriers of data isolation and promoting privacy protection. The legal principles, data minimization principle and purpose limitation principle, are embedded into the technical process of the system development. The distributed collaborative framework of federated learning uploads the updated parameters of the local model instead of original personal data, realizing local training and storage of data, and achieving such a great personal information protection effect that data can be utilizable while at the same time invisible. Due to potential network security attacks and inherent defects of machine learning algorithms black box, federated learning still faces the challenges of the principles of quality, fairness, and transparency. Federated learning is not a way to evade compliance obligations, but a feasible technical measure to reduce compliance risks of personal information. There still exist personal information protection obligations to be fulfilled when using federated learning framework. The determination of data ownership and responsibility allocation requires comprehensively consideration of the roles of each participant and the types of personal information processors.

Key words : federated learning; personal information protection; isolated data island; network security attack; collaborate and share