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TC: Small: Homomorphic Encryption for Cloud Privacy

TC: Small: Homomorphic Encryption for Cloud Privacy
TC:小:云隐私的同态加密
批准号:
1117590
负责人:
Berk Sunar
金额:
$31.29万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2014-07-31

项目摘要

项目成果

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中文摘要
翻译
该项目应用同态加密工具来提高云服务的安全性。云计算为公司节省了资金,因为它允许公司仅在需要服务时支付服务费用,而不是购买和维护可能没有多大用处的硬件/软件。获得这些节省的动力可能会改变企业和个人计算。 这种新的资源共享和复杂程度自然会产生与安全和隐私相关的新问题,这些问题既新颖又具有挑战性。 在最广泛的意义上,同态加密允许从不可信的来源借用计算能力。如果可行的话,这对云计算有着特殊的意义。软件所有者可以在此设置中使用同态加密来减少逆向工程的威胁并管理软件许可证。当在不受信任的云上可用时,软件以同态方式运行,数据以加密形式提交和返回,并且实例的执行对恶意用户具有高度鲁棒性。该项目消除了同态加密方案实施效率的不确定性,并将其提高到可用于云计算的程度。具体来说,该项目集中在三个模块:同态计算的指令集开发,同态方案的处理器特定优化,以及新同态方案的研究。
英文摘要
This project applies tools from homomorphic encryption to improve security in cloud services. Cloud computing saves money for companies by allowing them to pay for services only if they need them and just at the time that such services are needed, rather than purchasing and maintaining hardware/software that may see little use. The drive to gain these savings may transform both business and personal computing. This new level of resource sharing and complexity naturally generates new problems related to security and privacy, problems that are both novel and challenging. In the broadest sense, homomorphic encryption allows one to borrow computational power from an untrusted source. If made practical, this has especial implications for cloud computing. The software owner may employ homomorphic encryption in this setting to reduce the threat of reverse engineering and to administer software licenses. When made available over a untrusted cloud, the software is run homomorphically, data is submitted and returned in encrypted form, and the execution of instances is highly robust against malicious users. This project removes the uncertainties in the implementation efficiency of homomorphic encryption schemes and improves it to the point where it becomes practical for use in cloud computing. Specifically, the project centers on three modules: instruction set development for homomorphic computing, processor-specific optimizations for homomorphic schemes, and the investigation of new homomorphic schemes.
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