TWC: Medium: Collaborative: Computational Blinking - Computer Architecture Techniques for Mitigating Side Channels

TWC：媒介：协作：计算闪烁 - 用于缓解侧通道的计算机体系结构技术

• 批准号: 1563767
• 负责人: Ryan Kastner
• 金额: $ 81.6万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563767&HistoricalAwards=false
• 关键词: TWC; Medium; Collaborative; Computational; Blinking

Computer systems increasingly perform operations on critical and confidential data. Despite best efforts to protect this information, the side effects of computations using this data, e.g., the computation time, the power consumption, electromagnetic radiation, thermal emanations, and acoustics, can be used to decipher secret information even when it is encrypted. Power analysis attacks are particularly powerful and have been shown time and again to be able to quickly and reliably extract the most critical information from otherwise secure devices including smart cards, RFIDs, microcontrollers, microprocessors, and other computing hardware.Drawing inspiration from the human body, the proposed research will develop new techniques to mitigate power analysis attacks through a technique called computational blinking. An average person blinks 15-20 times per minute; in fact our eyes are closed around 10% of our waking hours due to blinking. With each and every blink, sections of our brain are momentarily "powered off," yet we are rarely even aware of these near continuous interruptions. Our techniques replicate this same, seemingly imperceivable interleaving of "connected" and "disconnected" states in a computer system through the dynamic reconfiguration of the electrical network powering a device. During a computational blink, a portion of the processor is momentarily electrically disconnected from the rest of the system making it more difficult to modify or examine the side effects (e.g., power consumption) of that computation. Our innovative circuits, architectures, run-time systems, programming frameworks, design tools, and measurement methodologies make these intermittent disconnections just as imperceivable in computing systems.

计算机系统越来越多地对关键和机密数据执行操作。尽管尽了最大努力保护该信息，但使用该数据进行计算的副作用，例如计算时间、功率消耗、电磁辐射、热辐射和声学，即使在加密的情况下也可以用于解密秘密信息。功率分析攻击特别强大，一次又一次地被证明能够快速可靠地从其他安全设备中提取最关键的信息，包括智能卡、RFID、微控制器、微处理器和其他计算硬件。这项拟议的研究从人体中汲取灵感，将开发一种新的技术，通过一种称为计算眨眼的技术来缓解功率分析攻击。普通人每分钟眨眼15-20次；事实上，在我们醒着的时间里，大约10%的时间是由于眨眼而闭上的。每眨眼一眼，我们大脑的部分区域就会暂时关闭，但我们甚至很少意识到这些近乎持续的干扰。我们的技术通过为设备供电的电力网络的动态重新配置，在计算机系统中复制了同样的、似乎难以察觉的“连接”和“断开”状态的交错。在计算眨眼期间，处理器的一部分与系统的其余部分暂时电断开，使得修改或检查该计算的副作用(例如，功耗)变得更加困难。我们的创新电路、架构、运行时系统、编程框架、设计工具和测量方法使这些间歇性断开在计算系统中同样不可察觉。