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.

物理上无统治的功能（PUF）是计算机硬件电路或硬件安全性原始电路，可以很容易地将其内置到计算机芯片中。预计它们对于确保物联网设备之间的下一代通信 /身份验证协议至关重要。但是，许多PUF研究本质上是临时和实验性的，对其优势和限制的基本了解很少。例如，尽管理想情况下，PUF的行为应该是不可预测的，因此不可统治，但某些“强” PUF被证明是“机器可学习的”。这意味着，通过偷听PUF在通信阶段中显示的大量数据，可以高准确地预测PUF的行为，从而使攻击者能够模仿PUF的行为，从而对安全基础构成可怕的挑战。为什么不完全理解这是可能的，而该项目的目标是开发一个新的框架，以系统的理论解释方式理解PUF的基本限制和属性。这有可能改变如何以基本方式设计和使用强大的PUF来进行身份验证：临时方法将被证明是最佳设计所取代的，以构建在声音理论上，以利用这些功能的全部潜力。PUF可以作为基本的安全性原始措施作为基本的安全性基础，当在芯片上大量构造的设备在较低的数字上提供了量身式的“ exploinity” exploinity“ exploiting”。该PUF“指纹”与简单条形码相比提供了更丰富的内容，它通过提供了可能“挑战”输入位向量的唯一输出功能。当每个输入与架构配置中的随机元素相互作用以产生“响应”时，PUF提供的这种挑战反应对（CRP）可用于唯一识别或验证设备。研究人员的目的是通过封闭形式的表达方式进行分析 - - 如何使PUF了解一组（小）观察到的CRP的知识。通过两位具有计算机工程专业知识和信息理论专业知识的女性研究人员之间的新合作解决了这个问题。 PUF将以统计学的模型进行建模，这将允许在PUF对一个（或更多）挑战的反应的条件概率质量函数（以及相关的最佳预测指标）衍生出来，鉴于对一个（或更多）其他挑战的响应的了解（S）奖励（s）奖励，本律师奖通过评估了NSF的法定任务，反映了对CRIPAIL的支持，并反映出了该奖项的知识范围。