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CT-ISG: Collaborative Research: Towards Trustworthy Database Systems

CT-ISG: Collaborative Research: Towards Trustworthy Database Systems
CT-ISG:协作研究:迈向可信赖的数据库系统
批准号:
1243971
负责人:
Feifei Li
金额:
$0.28万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-04-01 至 2012-09-30

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中文摘要
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英文摘要
Answers to database queries often form the basis for critical decision-making. To improve efficiency and reliability, answers to these queries can be provided by distributed servers close to the querying clients. However, because of the servers' ubiquity, the logistics associated with fully securing them may be prohibitive; moreover, when the servers are run by third parties, the clients may not trust them as much as they trust the original data owners. Thus, the authenticity of the answers provided by servers in response to clients' queries must be verifiable by the clients. More generally, database responses are more useful if they contain the evidence of their own correctness. For example, this enables a consumer to provide her own credit report to a creditor without having the creditor request it from the reporting agency to establish the validity of the report. This project is developing methods for authenticating the validity and authenticity of a variety of database queries, including general relational, data cube, and spatio-temporal queries. Furthermore, techniques that use powerful cryptographic primitives are being developed for providing authentication and confidentiality. This research will enable utilization of this infrastructure in applications where users must rely on the authenticity of the answer, such as in financial systems, network monitoring, traffic control, or applications yet to be imagined. The results of this project will be disseminated through publications in journals and conferences. Furthermore, source code of these methods, in the form of libraries, will be made available over the web.
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NSF DSSP Workshop: Data Science for Secure and Privacy-Aware Big Data Management and Mining
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CIF21 DIBBs: STORM: Spatio-Temporal Online Reasoning and Management of Large Data
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