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due to the development of network technology, inducing the appearance of big data. Experiments have shown that deep mining and analysis on large datasets would introduce great benefits. Although cloud computing supports data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to cloud servers. Meanwhile, the returned analysis result suffers from malicious inference attacks and also discloses user privacy. In this paper, to conquer the above privacy issues, we propose a general framework for<jats:sans-serif> P<\/jats:sans-serif>reserving<jats:sans-serif> M<\/jats:sans-serif>ultiparty<jats:sans-serif> D<\/jats:sans-serif>ata<jats:sans-serif> P<\/jats:sans-serif>rivacy (<jats:sans-serif>PMDP<\/jats:sans-serif> for short) in cloud computing. The<jats:sans-serif> PMDP<\/jats:sans-serif> framework can protect numeric data computing and publishing with the assistance of untrusted cloud servers and achieve delegation of storage simultaneously. Our framework is built upon several cryptography primitives (e.g., secure multiparty computation) and differential privacy mechanism, which guarantees its security against semihonest participants without collusion. We further instantiate<jats:sans-serif> PMDP<\/jats:sans-serif> with specific algorithms and demonstrate its security, efficiency, and advantages by presenting security analysis and performance discussion. Moreover, we propose a security enhanced framework<jats:sans-serif> sPMDP<\/jats:sans-serif> to resist malicious inside participants and outside adversaries. We illustrate that both<jats:sans-serif> PMDP<\/jats:sans-serif> and<jats:sans-serif> sPMDP<\/jats:sans-serif> are reliable and scale well and thus are desirable for practical applications.<\/jats:p>","DOI":"10.1155\/2017\/6097253","type":"journal-article","created":{"date-parts":[[2017,12,12]],"date-time":"2017-12-12T18:44:19Z","timestamp":1513104259000},"page":"1-14","source":"Crossref","is-referenced-by-count":20,"title":["PMDP: A Framework for Preserving Multiparty Data Privacy in Cloud Computing"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7439-782X","authenticated-orcid":true,"given":"Ji","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China"},{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100088, 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