论文标题
政府和公共部门部署的机器学习模型的安全和隐私注意事项（白皮书）
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
论文作者
论文摘要
随着机器学习成为一种更加主流技术，政府和公共部门的目标是利用机器学习的力量，通过彻底改变公共服务来推进其任务。鉴于他们提供的服务的重要性，激励政府用例需要特殊考虑来实施。这些应用不仅会部署在需要保护机制的潜在敌对环境中，而且还需要政府透明度和问责制计划，从而进一步使这种保护措施复杂化。 在本文中，我们描述了未知信任度的用户与机器学习模型（部署在政府和公共部门中的机器学习模型）之间的不可避免的互动，可以通过两种主要方式危害系统：通过损害完整性或侵犯隐私性。然后，我们简要概述了可能的攻击和防御方案，最后提出建议和准则，曾经考虑的可以增强提供服务的安全性和隐私。
As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by revolutionizing public services. Motivational government use cases require special considerations for implementation given the significance of the services they provide. Not only will these applications be deployed in a potentially hostile environment that necessitates protective mechanisms, but they are also subject to government transparency and accountability initiatives which further complicates such protections. In this paper, we describe how the inevitable interactions between a user of unknown trustworthiness and the machine learning models, deployed in governments and public sectors, can jeopardize the system in two major ways: by compromising the integrity or by violating the privacy. We then briefly overview the possible attacks and defense scenarios, and finally, propose recommendations and guidelines that once considered can enhance the security and privacy of the provided services.
