在人工智能和量子计算时代的基因组隐私和安全性
Richard Annan1, Justin Noland1, Kamaria Perkins1
1Department of Computer Science, North Carolina A&T State University, 1601 East Market St, Greensboro, NC 27411 USA.
概括
由于访问的增加,基因组数据的隐私至关重要. 这篇评论强调了人工智能和量子威胁,探索机器学习解决方案,如差异隐私和联合学习,以保护敏感的遗传信息.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 网络安全 网络安全
背景情况:
- 测序技术的进步导致公开可用的基因组数据激增.
- 增加数据可访问性引发了遗传数据库的重大隐私和安全问题.
- 当前的数据存储和共享实践显示出对网络攻击和内部漏洞的脆弱性.
研究的目的:
- 审查和分析基因组数据保护中的漏洞.
- 检查新出现的威胁,包括人工智能驱动的攻击和量子计算风险.
- 探索用于增强基因组数据安全性和隐私的机器学习方法.
主要方法:
- 分析当前的基因组数据存储和共享漏洞.
- 对数据隐私的机器学习算法的审查 (例如,差异隐私,联合学习,生成对抗网络).
- 检查人工智能驱动的威胁和量子计算漏洞.
主要成果:
- 在缓解常见的隐私侵犯,如重新识别和推断攻击方面取得了进展.
- 持续存在的漏洞仍然存在,特别是针对模型倒置和会员推断攻击等高级威胁.
- 机器学习方法在平衡隐私保护与数据实用性方面表现有前途.
结论:
- 结合立法框架和先进技术的综合方法对于基因组隐私至关重要.
- 迫切需要进一步的研究来开发抗量子加密方法和区块链集成的安全性.
- 基因组学研究人员必须优先考虑数据隐私和安全,以便负责任地处理数据.
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