Sharing more, protecting more: Three lessons from the Safe Data Technologies project
On June 4, the Department of Commerce issued a disclosure avoidance order that has reignited the heated discussion on the tradeoffs among data privacy, access, and accuracy .
6月4日，美国商务部发布了一项避免披露令，再次引发了关于数据隐私、访问和准确性之间权衡的热议。
The order has focused on the choice of suppressing and coarsening the data instead of using “noise infusion,” which includes data swapping and synthetic data to protect official statistics and data.
该命令的重点是选择抑制和粗化数据，而不是使用“噪音注入”，其中包括数据交换和合成数据以保护官方统计数据和数据。
The challenges of balancing these competing needs are not unique to the U.S. Census Bureau and Bureau of Economic Analysis.
平衡这些相互竞争的需求的挑战并非美国人口普查局和经济分析局所独有。
Across the federal statistical system, agencies must provide timely evidence while maintaining strong privacy protections in an environment where data are fragmented across agencies, programs, and legal authorities.
在整个联邦统计系统中，各机构必须提供及时的证据，同时在数据分散于各机构、项目和法律当局的环境中保持强有力的隐私保护。
This situation raises the question of what other privacy-enhancing technologies and techniques (PETs) are available to balance privacy, access, and accuracy without withholding more data or reducing the information to levels that are not useful.
这种情况提出了一个问题：有哪些其他隐私增强技术和技巧 (PET) 可以用来平衡隐私、访问和准确性，而不会保留更多数据或将信息减少到无用的水平。
The Safe Data Technologies (SDT) project , a collaboration involving the Statistics of Income Division (SOI) at the IRS, the Urban Institute, and other partners, offers a case study for how new PETs can move from theoretical concepts into operational tools and provide evidence on appropriate PETs to improve the tradeoff.
安全数据技术 (SDT) 项目是美国国税局收入统计司 (SOI)、城市研究所和其他合作伙伴的合作项目，提供了一个案例研究，说明新的 PET 如何从理论概念转变为操作工具，并提供有关适当 PET 的证据以改善权衡。
Although the PETs developed for SDT focused on expanding access to confidential tax data through synthetic data and privacy-preserving validation servers , SDT provides lessons learned that extend well beyond administrative tax data and synthetic data applications.
尽管为 SDT 开发的 PET 侧重于通过合成数据和隐私保护验证服务器扩大对机密税务数据的访问，但 SDT 提供的经验教训远远超出了行政税务数据和合成数据应用程序的范围。
In other words, SDT may help inform ongoing debates about the role of synthetic data under the order and across a wider range of federal statistical and data access contexts.
换句话说，SDT 可能有助于为有关合成数据在该命令下以及更广泛的联邦统计和数据访问环境中的作用的持续辩论提供信息。
This blog presents three lessons that other agencies across the federal statistical system and potentially others, such as state statistical systems, may learn.
本博客介绍了联邦统计系统的其他机构以及州统计系统等其他机构可能学到的三个教训。
One reason SDT has advanced beyond a research prototype is that it was supported through multiple funding streams and partnership models, which has been both valuable and challenging.
SDT 超越研究原型的原因之一是它得到了多种资金流和合作伙伴模式的支持，这既有价值又具有挑战性。
The opportunity lies in drawing on different types of support at different stages of development, where the challenge is maintaining a consistent long-term vision while balancing the priorities and expectations of the multiple funders.
机会在于在不同的发展阶段利用不同类型的支持，挑战在于保持一致的长期愿景，同时平衡多个资助者的优先事项和期望。
In our case, those partnerships aligned with different stages of the project.
就我们而言，这些合作伙伴关系与项目的不同阶段相一致。
Early work was supported by Arnold Ventures and the Alfred P.
早期工作得到了 Arnold Ventures 和 Alfred P.
Sloan Foundation, whose investments enabled exploratory research and prototype development.
斯隆基金会，其投资促进了探索性研究和原型开发。
Sloan Foundation support has also advanced related efforts through the Re-Engineering Statistics using Economic Transactions and Economic Indicators Initiative projects, creating further opportunities to identify lessons learned and build complementary capabilities.
斯隆基金会的支持还通过利用经济交易和经济指标重新设计统计倡议项目推进了相关工作，为总结经验教训和建立互补能力创造了更多机会。
Those initial efforts generated the evidence needed to secure funding from the National Science Foundation’s National Center for Science and Engineering Statistics (NSF/NCSES) and, ultimately, direct investment from the SOI as funding became available.
这些最初的努力产生了获得美国国家科学基金会国家科学与工程统计中心 (NSF/NCSES) 资助所需的证据，并最终在资金到位时获得 SOI 的直接投资。
These funding sources served different purposes.
这些资金来源有不同的目的。
Philanthropic funding created space for experimentation.
慈善资金为实验创造了空间。
Research grants supported technical advancement and evaluation.
研究补助金支持技术进步和评估。
Federal agency funding had operational requirements, implementation, and long-term sustainability.
联邦机构的资金有运营要求、实施和长期可持续性。
For federal statistical agencies and developers alike, the lesson is that innovation should be viewed as a multi-stage process that depends on strong, sustained partnerships.
对于联邦统计机构和开发人员来说，教训是创新应被视为一个多阶段的过程，依赖于强大、持续的伙伴关系。
Different types of partnerships become valuable at different points, from initial proof of concept to eventual deployment.
不同类型的合作伙伴关系在不同的阶段变得有价值，从最初的概念验证到最终的部署。
In other words, the project’s success depends on the collaboration of multiple partners.
换句话说，项目的成功取决于多个合作伙伴的协作。
SOI, as an official statistical agency, along with academic and nonprofit researchers, technology developers, and funders, each contributed unique expertise, resources, and perspectives that helped advance the project from research to operational use while maintaining a shared long-term vision.
SOI 作为官方统计机构，与学术和非营利研究人员、技术开发人员和资助者一起，各自贡献了独特的专业知识、资源和观点，帮助推进项目从研究到运营使用，同时保持共同的长期愿景。
Many of the challenges faced by federal statistical agencies are similar.
联邦统计机构面临的许多挑战是相似的。
Agencies must protect confidential information, provide access to approved users, comply with legal requirements, and operate under resource constraints, including limited staff time and computing capacity.
各机构必须保护机密信息，向批准的用户提供访问权限，遵守法律要求，并在资源限制下运作，包括有限的员工时间和计算能力。
These shared challenges create opportunities to develop PETs that can reduce administrative burden and accelerate data access and analysis.
这些共同的挑战为开发 PET 创造了机会，可以减轻管理负担并加速数据访问和分析。
However, the SDT project demonstrates that successful adoption depends on much more than whether a technology works in theory.
然而，SDT 项目表明，成功采用不仅仅取决于技术在理论上是否有效。
As the project matured, the SDT team found after engaging with others outside of SOI that implementation requirements varied substantially across government entities , from local agencies to federal statistical organizations.
随着项目的成熟，SDT 团队在与 SOI 之外的其他人接触后发现，从地方机构到联邦统计组织的政府实体之间的实施要求存在很大差异。
Infrastructure, governance processes, hardware, security requirements, and staff expertise all influence whether a system can be successfully deployed and maintained.
基础设施、治理流程、硬件、安全要求和员工专业知识都会影响系统能否成功部署和维护。
This challenge becomes even more apparent when considering how technologies developed for one agency can be adapted for another.
当考虑如何为一个机构开发的技术适用于另一个机构时，这一挑战变得更加明显。
A system designed around administrative tax data and SOI workflows may require significant modifications before it can be used by the Census Bureau, Bureau of Economic Analysis, Bureau of Labor Statistics, or state data systems.
围绕行政税务数据和 SOI 工作流程设计的系统可能需要进行重大修改才能供人口普查局、经济分析局、劳工统计局或州数据系统使用。
As the core SDT team considers expanding the work beyond federal tax data, a major focus has been identifying which components can be standardized or packaged for broader use and which aspects must remain tailored to local environments.
由于核心 SDT 团队考虑将工作扩展到联邦税务数据之外，因此主要重点是确定哪些组件可以标准化或打包以供更广泛的使用，以及哪些方面必须根据当地环境进行定制。
To support this effort, the team is in the process of engaging state and local partners to assess technical, governance, legal, and operational barriers to adoption; provide feedback on implementation requirements and governance frameworks; and participate in project reviews and testing activities.
为了支持这项工作，该团队正在与州和地方合作伙伴合作，评估采用的技术、治理、法律和运营障碍；提供有关实施要求和治理框架的反馈；并参与项目评审和测试活动。
The broader lesson is that developers should engage a diverse set of stakeholders and use cases early in the process.
更广泛的教训是，开发人员应该在流程的早期就吸引不同的利益相关者和用例。
Building PETs that work in a single environment is not enough.
构建在单一环境中工作的 PET 还不够。
Adoption of PETs requires understanding where agencies share common needs, where they differ, and how a solution can be adapted across those differences without losing its core functionality.
采用 PET 需要了解各机构在哪些方面有共同的需求，在哪些方面存在差异，以及如何在不丧失其核心功能的情况下适应这些差异。
New technologies and techniques often receive the most attention, but one of the most important lessons from the SDT project is that organizational capacity matters as much or more than the technology and techniques themselves.
新技术和技巧往往受到最多的关注，但 SDT 项目最重要的教训之一是组织能力与技术和技巧本身同等重要，甚至更重要。
For instance, implementing new PETs introduces new questions for agency leaders and data stewards.
例如，实施新的 PET 给机构领导者和数据管理员带来了新的问题。
How should privacy risks be evaluated?
隐私风险该如何评估？
How much accuracy is sufficient for a given use case?
对于给定的用例，多少准确度就足够了？
What governance processes are needed before a new system can be trusted in a production environment?
在生产环境中信任新系统之前需要哪些治理流程？
The SDT project has invested heavily in helping answer these questions while engaging and training SOI staff.
SDT 项目投入了大量资金来帮助回答这些问题，同时吸引和培训 SOI 员工。
Through these engagements, the project created documentation, open-source tools, several internal and external dissemination materials, stakeholder engagement, technical guidance, and advisory activities that help agencies understand both the capabilities and limitations of the technology.
通过这些参与，该项目创建了文档、开源工具、一些内部和外部传播材料、利益相关者参与、技术指导和咨询活动，帮助机构了解该技术的能力和局限性。
We have carried this approach forward as we adapt the synthetic data and validation server framework for state agencies , where we focus on governance requirements, implementation guidance, operational planning, workforce readiness, and hands-on technical assistance.
我们在为国家机构调整合成数据和验证服务器框架时继续推进这种方法，重点关注治理要求、实施指南、运营规划、劳动力准备情况和实际技术援助。
Through training sessions , co-design activities, and ongoing support, our goal is to help agency staff develop the knowledge and confidence needed to operate and govern these systems without our support.
通过培训课程、共同设计活动和持续的支持，我们的目标是帮助机构工作人员掌握在没有我们支持的情况下操作和管理这些系统所需的知识和信心。
The lesson is that successful adoption requires more than acquiring a new technology.
教训是，成功采用需要的不仅仅是获得新技术。
Agencies need the tools, training, governance frameworks, and internal expertise necessary to make informed decisions about implementation and long-term use once a contractor or vendor is no longer around.
一旦承包商或供应商不再存在，各机构需要必要的工具、培训、治理框架和内部专业知识，以便就实施和长期使用做出明智的决策。
This is especially salient today , as agencies are adjusting their disclosure policies .
这一点在今天尤其突出，因为各机构正在调整其披露政策。
New PETs or other technologies alone will not solve organizational challenges.
仅靠新的 PET 或其他技术并不能解决组织面临的挑战。
Lasting impact comes from building the capacity within agencies to evaluate, adopt, and manage new approaches effectively.
持久的影响来自于机构内部有效评估、采用和管理新方法的能力建设。
The future of federal statistical agencies still requires navigating fragmented data systems, evolving privacy regulations, constrained resources, and growing demand for timely evidence.
联邦统计机构的未来仍然需要应对分散的数据系统、不断发展的隐私法规、有限的资源以及对及时证据不断增长的需求。
SDT project illustrates that moving from innovation to impact requires three things: multiple strong partnerships that support different stages of development both technical and funding support, technologies and techniques designed with real-world implementation in mind, and investments in the people and governance structures responsible for operational decisions.
SDT 项目表明，从创新转向影响力需要三件事：支持不同发展阶段的多个强有力的合作伙伴关系（包括技术和资金支持）、设计时考虑到实际实施的技术和工艺，以及对负责运营决策的人员和治理结构的投资。
The importance of these lessons learned extends beyond SDT.
这些经验教训的重要性超出了特殊和差别待遇的范围。
For example, NSF/NCSES’ SEDSyn-23 project brings together NCSES, Urban Institute, RTI International, RAND, and Vassar College to develop and evaluate synthetic versions of the Survey of Earned Doctorates.
例如，NSF/NCSES 的 SEDSyn-23 项目汇集了 NCSES、Urban Institute、RTI International、RAND 和瓦萨学院，开发和评估获得博士学位调查的综合版本。
Similar to SDT, this project demonstrates how collaboration among statistical agencies, researchers, and technology experts, combined with a focus on real-world implementation and investments in people and governance structures, is essential for advancing PETs from research to practice.
与 SDT 类似，该项目展示了统计机构、研究人员和技术专家之间的合作，加上对现实世界实施以及对人员和治理结构的投资的关注，对于推动 PET 从研究到实践至关重要。
For agencies considering adopting PETs, these lessons demonstrate that long-term success depends on building the partnerships, expertise, and governance frameworks needed to sustain innovation long after a pilot project ends.
对于考虑采用 PET 的机构来说，这些经验教训表明，长期成功取决于建立在试点项目结束后很长时间内维持创新所需的合作伙伴关系、专业知识和治理框架。