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CAREER: Computational RFID for Securing Zero-Power Pervasive Devices

CAREER: Computational RFID for Securing Zero-Power Pervasive Devices
职业:用于保护零功耗普及设备的计算 RFID
批准号:
0845874
负责人:
Kevin Fu
金额:
$40.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-15 至 2013-03-31

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中文摘要
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英文摘要
This research advances the knowledge and capability for securelyperforming general-purpose computation and communication on zero-powerdevices that rely on harvested energy stored in extremely smallreservoirs such as capacitors. A type of zero-power device,computational RFIDs (CRFIDs) harvest RF energy and endure continualinterruptions to power. Complete loss of RAM on the order of everysecond makes the notion of a computational checkpoint fundamental tothis model of computing. A significant problem is how to performeffective computation and checkpoints in a secure andprivacy-preserving manner. The most fundamental question is: how tosecurely make forward progress of computation on zero-power devices?The exploration of checkpointing strategies that exploit theenergy-centric properties of computational RFIDs will lay a solidfoundation for designing secure software and systems for pervasivedevices. Beyond security and privacy, the research seeks to discoverwhat kinds of computational problems can be solved in a practicalsense on extremely resource-constrained devices. While theexperiments focus on computational RFIDs, many of the techniques willapply broadly to devices with batteries as well.The impact of this work will lead to improved security for computationin low-power, untrusted infrastructure. The expected results will bepublished in venues at the intersection of security & privacy andlow-power computation. Potential applications include improvedsecurity and privacy for (1) implantable medical devices and (2)sensors embedded in concrete components of bridges and roads.
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CAREER: Computational RFID for Securing Zero-Power Pervasive Devices
CT-ISG: Improving Security and Privacy in Pervasive Healthcare
国内基金
海外基金
Computational Methods for Analyzing Toponome Data