根据加拿大隐私法规对合成数据生成,使用和披露的评估
Lisa Pilgram1,2,3, Anita Fineberg4,5, Elizabeth Jonker1
1Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada.
概括
合成数据生成 (SDG) 并未被加拿大隐私法明确涵盖,造成不确定性和潜在风险. 建议包括立法行为准则,以澄清SDG规则并保护隐私权.
科学领域:
- 隐私法 隐私法 隐私法 隐私法
- 数据科学数据科学数据科学
- 信息治理 信息治理
背景情况:
- 合成数据生成 (SDG) 是研究和创新的关键加速器.
- 可持续发展目标提供保护隐私的数据共享,但也引发了新的隐私问题.
- 隐私法适用于SDG的不确定性阻碍了其采用,并危及个人隐私.
研究的目的:
- 分析加拿大联邦隐私法对待SDG和合成数据的处理.
- 确定有关SDG的隐私法中的监管漏洞.
- 提出建议,以解决发现的差距,并确保适当的SDG应用.
主要方法:
- 加拿大联邦隐私法规的法律分析.
- 检查与SDG相关的公平信息实践.
- 对合成数据的识别性概念的评估.
主要成果:
- 加拿大隐私立法中没有明确解决SDG问题.
- 目前尚不清楚SDG是否需要同意,尽管公平信息实践适用.
- 生成的数据可能超出现行法律的范围,因为非个人,但识别能力仍然模两可.
结论:
- 可识别性定义的模糊性对个人隐私构成风险,并阻碍了像SDG这样有益的技术.
- 建议制定立法行为准则,以澄清SDG的隐私义务.
- 解决这些差距对于平衡创新与隐私保护至关重要.
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