CSR: Small: Privacy and Security for MapReduce Clouds with PASMAC

CSR：小：使用 PASMAC 实现 MapReduce 云的隐私和安全

• 批准号: 1218197
• 负责人: Guevara Noubir
• 金额: $ 49.44万
• 依托单位: Northeastern University
• 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
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218197&HistoricalAwards=false
• 关键词: CSR; Small; Privacy; Security; MapReduce

This research targets the design and evaluation of protocols forsecure, privacy-preserving data analysis in an untrustedcloud. Therewith, the user can store and query data in the cloud,preserving privacy and integrity of outsourced data and queries. ThePIs specifically address a real-world cloud framework: Google'sprominent MapReduce paradigm.Traditional solutions for single server setups and related work on,e.g., fully homomorphic encryption, are computationally too heavy anduneconomical and offset cloud advantages. The PIs' rationale is todesign new protocols tailored to the specifics of the MapReducecomputing paradigm. The PIs' methodology is twofold. First, the PIsdesign new protocols that allow the cloud user to specify dataanalysis queries for typical operations such as searching, patternmatching or counting. For this, the PIs extend privacy-preservingtechniques, e.g., private information retrieval or order preservingencryption. Second, the PIs design protocols guaranteeing genuinenessof data retrieved from the cloud. Using cryptographic accumulators,users can verify whether data has not been tampered with. Besidesdesign, the PIs also implement a prototype that is usable in arealistic setting with MapReduce.The outcome of this project enables privacy-preserving operations andsecure data storage in a widely-used cloud computing framework, thusremove one major adoption obstacle, and make cloud computing availablefor a larger community.

本研究的目标是在不受信任的云中设计和评估安全、隐私保护的数据分析协议。因此，用户可以在云中存储和查询数据，保护外包数据和查询的隐私和完整性。PI专门针对现实世界的云框架：Google的著名MapReduce范式。传统的单服务器设置解决方案和相关工作，例如，全同态加密，计算量太大，不经济，抵消了云的优势。 PI的基本原理是根据MapReduce计算范例的具体情况设计新的协议。 PI的方法是双重的。首先，PI设计了新的协议，允许云用户为典型操作（如搜索、模式匹配或计数）指定数据分析查询。为此，PI扩展了隐私保护技术，例如，私人信息检索或订单加密。 第二，PI设计协议，保证从云中检索数据的一致性。使用加密累加器，用户可以验证数据是否未被篡改。 除了设计之外，PI还实现了一个原型，该原型可以在现实环境中使用MapReduce。该项目的成果使隐私保护操作和安全数据存储在广泛使用的云计算框架中，从而消除了一个主要的采用障碍，并使云计算可用于更大的社区。