论文标题
部署差异隐私时的随机性问题
Randomness Concerns When Deploying Differential Privacy
论文作者
论文摘要
美国人口普查局正在利用差异隐私（DP）来保护针对2020年人口和住房人口普查收集的机密受访者数据。人口普查局的DP系统是在披露避免系统（DAS）中实施的，需要随机数的来源。我们估计2020年的人口普查将需要大约90TB的随机字节来保护人和家居桌子。尽管密码学和DP之间存在关键差异，但它们对随机性有相似的要求。我们回顾了确定性计算机上随机数的历史记录，包括冯·诺伊曼（Von Neumann）的“中间方面”方法，梅尔森·吐斯（Mersenne Twister）（MT19937）（以前是默认的numpy随机数生成器，我们认为这是不可接受的，用于生产隐私保护系统）和linux /dev /ureandom设备。我们还审查了硬件随机数生成器方案，包括使用所谓的“熔岩灯”和Intel Secure Key RDRAND指令。我们最终提出了我们的计划，以使用AES-CTR-DRBG在Amazon Web服务（AWS）环境中生成随机位，该计划通过与 /dev /urandom的混合和Intel Secure Key Rdseed指令进行混合，这是我们愿意依靠可信赖的硬件实施的愿望，这是我们的外部审阅者的不安全，在这些硬件中的不安全是需要硬件的实施以及许多硬件生成的生成。
The U.S. Census Bureau is using differential privacy (DP) to protect confidential respondent data collected for the 2020 Decennial Census of Population & Housing. The Census Bureau's DP system is implemented in the Disclosure Avoidance System (DAS) and requires a source of random numbers. We estimate that the 2020 Census will require roughly 90TB of random bytes to protect the person and household tables. Although there are critical differences between cryptography and DP, they have similar requirements for randomness. We review the history of random number generation on deterministic computers, including von Neumann's "middle-square" method, Mersenne Twister (MT19937) (previously the default NumPy random number generator, which we conclude is unacceptable for use in production privacy-preserving systems), and the Linux /dev/urandom device. We also review hardware random number generator schemes, including the use of so-called "Lava Lamps" and the Intel Secure Key RDRAND instruction. We finally present our plan for generating random bits in the Amazon Web Services (AWS) environment using AES-CTR-DRBG seeded by mixing bits from /dev/urandom and the Intel Secure Key RDSEED instruction, a compromise of our desire to rely on a trusted hardware implementation, the unease of our external reviewers in trusting a hardware-only implementation, and the need to generate so many random bits.
