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

CIF：小：分析预测来自少数已知 CRP 的强烈 PUF 反应

基本信息

批准号：
1909547
负责人：
Natasha Devroye
金额：
$ 23.99万
依托单位：
University of Illinois at Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909547&HistoricalAwards=false
关键词：
CIF Small Analytically Predicting Strong

项目摘要

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.

物理不可克隆功能（Physically Unclonable Functions，PUF）是计算机硬件电路或硬件安全原语，可以很容易地内置到计算机芯片中。预计它们将成为确保物联网设备之间的下一代通信/认证协议的关键。然而，许多PUF研究在本质上是临时的和实验性的，对它们的优势和局限性几乎没有基本的了解。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已被证明是“机器可学习的”。这意味着，通过在通信阶段偷听PUF所展示的大量数据，可以高精度地预测PUF的行为，从而使攻击者能够模仿PUF行为，对安全基础构成严峻挑战。为什么这是可能的还没有完全理解，该项目的目标是开发一个新的框架，以系统的，理论上解释的方式，了解PUFs的基本限制和属性。这有可能改变应该如何设计强大的PUF并以基本方式用于认证：特别的方法将被建立在可靠理论上的可证明的最佳设计所取代，以充分利用这些功能的全部潜力。PUF作为基本的安全原语是有前途的，当在芯片上大规模制造时，可以通过利用制造过程中的随机无序来为设备提供低成本的数字“指纹”。这种PUF“指纹”通过提供可以用输入位向量“挑战”的独特输出函数，提供比简单条形码更丰富的内容。由于每个输入与架构配置中的随机元素交互以产生“响应”，因此由PUF提供的这种质询-响应对（CRP）可以用于唯一地标识或认证设备。研究人员的目标是找出-在封闭形式的表达式中分析-给定一组（小）观察到的CRP的知识，PUF如何变得可预测。这个问题是通过两名女性研究人员在计算机工程和信息理论方面的专业知识之间的新合作来解决的。将对PUF进行统计建模，这将允许推导条件概率质量函数在已知对一个（或多个）其他挑战的响应的情况下，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。