Page 319 - 《软件学报》2021年第11期
P. 319
刘旭红:高效安全向量计算及其推广 3645
[27] Du WL, Zhan ZJ. A practical approach to solve secure multi-party computation problems. In: Proc. of the 2002 Workshop on New
Security Paradigms. Association for Computing Machinery, 2002. 127−135. https://doi.org/10.1145/844102.844125
[28] Shaneck M, Kim Y. Efficient cryptographic primitives for private data mining. In: Proc. of the 43rd Hawaii Int’l Conf. on System
Sciences. Hawaii: IEEE, 2010. 1−9. [doi: 10.1109/HICSS.2010.172]
[29] Zhu Y, Takagi T. Efficient scalar product protocol and its privacy-preserving application. Int’l Journal of Electronic Security
Digital Forensics, 2015,7(1):1−19. [doi: 10.1504/IJESDF.2015.067985]
[30] Goethals B, Laur S, Lipmaa H, Mielikäinen T. On private scalar product computation for privacy-preserving data mining. In: Proc.
of the Int’l Conf. on Information Security and Cryptology. Berlin, Heidelberg: Springer-Verlag, 2004. 104−120. [doi: 10.1007/114
96618_9]
[31] Yang B, Yang CH, Yu Y, Xie D. A secure scalar product protocol and its applications to computational geometry. Journal of
Computers, 2013,8(8):2018−2026. [doi: 10.4304/jcp.8.8.2018-2026]
[32] Dong CY, Chen LQ. A fast secure dot product protocol with application to privacy preserving association rule mining. In: Proc. of
the Advances in Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 2014. 606−617. [doi: 10.1016/0022-4804(81)900
76-7]
[33] Sheng G, Wen T, Guo Q, Yin Y. Privacy preserving inner product of vectors in cloud computing. Int’l Journal of Distributed
Sensor Networks, 2014,10(5):102−110.
[34] Liu F, Ng WK, Zhang W. Secure scalar product for big-data in mapreduce. In: Proc. of the 2015 IEEE 1st Int’l Conf. on Big Data
Computing Service and Applications. San Francisco: IEEE, 2015. 120−129. https://doi.org/10.1109/BigDataService.2015.9
[35] Li SD, Yang XL, Zuo XJ, Zhou SH, Kang J, Liu X. Privacy-preserving graphical similarity determination. Chinese Journal of
Electronics, 2017,45(9):2184−2189 (in Chinese with English abstract). [doi: 10.3969/j.issn.0372-2112.2017.09.019]
[36] Atallah MJ, Du WL. Secure multi-party computational geometry. In: Proc. of the 7th Int’l Workshop on Algorithms and Data
Structures. Berlin, Heidelberg: Springer-Verlag, 2001. 165−179. https://doi.org/10.1007/3-540-44634-6_16
[37] Liu W, Luo SS, Wang YB. Secure two-party vector dominance statistic protocol and its applications. Chinese Journal of
Electronics, 2010,38(11):2573−2577 (in Chinese with English abstract).
[38] Li SD, Zuo XJ, Yang XL, Gong LM. Secure vector dominance protocol and its applications. Chinese Journal of Electronics, 2017,
45(5):1117−1123 (in Chinese with English abstract). [doi: 10.3969/j.issn.0372-2112.2017.05.014]
[39] Damgard I, Jurik M. A length-flexible threshold cryptosystem with applications. In: Proc. of the 2003 Australasian Conf. on
Information Security and Privacy. Berlin, Heidelberg: Springer-Verlag, 2003. 350−364. https://doi.org/10.1007/3-540-45067-X_30
附中文参考文献:
[16] 刘良桂,孙辉,贾会玲,张宇,面向高效加密云数据排序搜索的类别分组索引方法.电子学报,2019,47(2):331−336. [doi: 10.3969/
j.issn.0372-2112.2019.02.011]
[19] 尹鑫,田有亮,王海龙.面向大数据定价的委托拍卖方案.电子学报,2018,46(5):1113−1120. [doi: 10.3969/j.issn.0372-2112.2018.05.
014]
[24] 周素芳,窦家维,郭奕旻,毛庆,李顺东.安全多方向量计算.计算机学报,2017,40(5):1134−1150. [doi: 10.11897/SP.J.1016.2017.
01134]
[35] 李顺东,杨晓莉,左祥建,周素芳,亢佳,刘新.保护私有信息的图形相似判定.电子学报,2017,45(9):2184−2189. [doi: 10.3969/j.issn.
0372-2112.2017.09.019]
[37] 刘文,罗守山,王永滨.安全两方向量优势统计协议及其应用.电子学报,2010,38(11):2573−2577.
[38] 李顺东,左祥建,杨晓莉,巩林明.安全向量优势协议及其应用.电子学报,2017,45(5):1117−1123. [doi: 10.3969/j.issn.0372-2112.
2017.05.014]
刘旭红(1992-),女,助教,主要研究领域为
应用数学,应用密码学.