CIF: Small: Analytically Predicting Strong PUF Responses from Few Known CRPs

物理不可克隆功能(PUF)是可以很容易地内置到计算机芯片中的计算机硬件电路或硬件安全原语。预计它们将成为确保物联网设备之间的下一代通信/身份验证协议的关键。然而，许多PUF研究都是临时的和实验性的，对它们的优势和局限性几乎没有根本的了解。例如，虽然理想情况下，PUF的行为应该是不可预测的，因此是不可克隆的，但一些“强”PUF已经被证明是“机器可学习的”。这意味着，通过在通信阶段无意中监听PUF展示的大量数据，可以高精度预测PUF的行为，从而使攻击者能够模仿PUF行为，对安全基础构成可怕的挑战。为什么这是可能的还没有完全理解，这个项目的目标是开发一个新的框架，以一种系统的、理论上解释的方式理解PUF的基本限制和性质。这有可能以根本的方式改变PUF的设计和用于身份验证的强度：特别方法将被基于可靠理论的可证明的最佳设计所取代，以充分利用这些功能的潜力。PUF作为基本的安全基元很有希望，当在芯片上大规模制造时，可能通过利用制造过程中的随机无序为设备提供低成本的数字“指纹”。这种PUF“指纹”通过提供独特的输出功能提供了比简单的条形码更丰富的内容，该功能可能会被输入位向量“挑战”。当每个输入与体系结构配置中的随机元素交互以产生“响应”时，由PUF提供的这种挑战-响应对(CRP)可用于唯一地标识或认证设备。研究人员的目标是找出-以封闭形式的解析表达式-在已知观察到的一组(小)CRP的情况下，PUF的可预测性如何。这一问题是通过两位在计算机工程和信息理论方面有专长的女性研究人员之间的新合作来解决的。PUF将被统计建模，这将允许推导出PUF对一个(或多个)挑战(S)的响应的条件概率质量函数(以及相关的可证明的最优预测值)，已知对一个(或多个)其他挑战(S)的响应。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Physically Unclonable Functions (PUFs) are computer hardware circuits, or hardware security primitives, that can easily be built into computer chips. They are expected to become essential in securing next generation communication / authentication protocols among Internet of Things devices. However, much PUF research has been ad hoc and experimental in nature, with little fundamental understanding of their strengths and limits. For example, while ideally the behavior of a PUF should be unpredictable, thus unclonable, some "strong" PUFs have been shown to be "machine learnable". This means that by overhearing a large amount of the data exhibited by the PUF during communication phases, it is possible to predict the behavior of the PUF with high accuracy, thus enabling an attacker to mimic the PUF behavior, posing a dire challenge to the security foundation. Why this is possible is not fully understood, and this project's goal is to develop a new framework for understanding, in a systematic, theoretically explained way, the fundamental limits and properties of PUFs. This has the potential to transform how strong PUFs should be designed and used for authentication in fundamental ways: ad hoc approaches will be replaced by provably optimal designs built on sound theory to leverage the full potential of these functions.PUFs are promising as fundamental security primitives that, when mass fabricated on chips, may provide devices with a low-cost digital "fingerprint" by exploiting the random disorder in the manufacturing process. This PUF "fingerprint" provides richer content than a simple barcode by offering a unique output function that may be "challenged" with an input bit vector. As each input interacts with the random elements in the architectural configuration to produce a "response," such challenge-response pairs (CRPs) provided by the PUF can be used to uniquely identify or authenticate a device. The investigators aim to find out---analytically in closed form expressions--- how predictable a PUF becomes given knowledge of a (small) set of observed CRPs. This problem is approached through a new collaboration between two female researchers with expertise in computer engineering and information theory. PUFs will be modeled statistically, which will allow for the derivation of conditional probability mass functions (and associated provably optimal predictors) of the PUF response to one (or more) challenge(s) given knowledge of the response to one (or more) other challenge(s).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.