Page 318 - 《软件学报》2021年第11期

3644 Journal of Software 软件学报 Vol.32, No.11, November 2021

[4] Goldreich O, Micali S, Wigderson A. How to play any mental game. In: Proc. of the 19th Annual ACM Conf. on Theory of
Computing. New York: ACM, 1987. 218−229. [doi: 10.1145/28395.28420]
[5] Goldreich O. Fundamental of Cryptography II: Basic Applications. London: Cambridge University Press, 2004. 599−729. [doi:
10.1017/CBO9780511721656]
[6] Tang CM, Shi GH, Yao ZA. Secure multi-party computation protocol for sequencing problem. Science China Information Sciences,
2011,54(8):1654−1662. [doi: 10.1007/s11432-011-4272-1]
[7] Toft T. Sub-linear, secure comparison with two non-colluding parties. In: Proc. of Int’l Conf. on Practice and Theory in Public Key
Cryptography. Berlin: Springer-Verlag, 2011. 174−191. [doi: 10.1007/978-3-642-19379-8_11]
[8] Yi X, Rao FY, Bertino E, Bouguettaya A. Privacy-preserving association rule mining in cloud computing. In: Proc. of the 10th
ACM Symp. on Information, Computer and Communications Security. Association for Computing Machinery, 2015. 439−450. [doi:
10.1145/2714576. 2714603]
[9] Li YP, Chen MH, Li QW, Zhang W. Enabling multilevel trust in privacy preserving data mining. IEEE Trans. on Knowledge and
Data Engineering, 2012,24(9):1598−1612. [doi: 10.1109/TKDE.2011.124]
[10] Mehmed K. Data Mining: Concepts, Models, Methods, and Algorithms. Hoboken: John Wiley & Sons, 2011. 1−25. [doi: 10.1002/
9781118029145.ch1]
[11] Du WL, Atallah MJ. Secure multi-party computation problems and their applications: A review and open problems. In: Proc. of the
2001 Workshop on New Security Paradigms. ACM, 2001. 13−22. [doi: 10.1145/508171.508174]
[12] Li SD, Wu CY, Wang DS, Dai YQ. Secure multiparty computation of solid geometric problems and their applications. Information
Sciences, 2014,282:401−413. [doi: 10.1016/j.ins.2014.04.004]
[13] Li SD, Wang DS, Dai YQ. Efficient secure multiparty computational geometry. Chinese Journal of Electronics, 2010,19(2):
324−328.
[14] Liu YJ, Luo X, Joneja A, Ma CX, Fu XL, Song DW. User-adaptive sketch-based 3D CAD model retrieval. IEEE Trans. on
Automation Science and Engineering, 2013,10(3):783−795. [doi: 10.1109/TASE.2012.2228481]
[15] Fong PK, Weber-Jahnke JH. Privacy preserving decision tree learning using unrealized data sets. IEEE Trans. on Knowledge and
Data Engineering, 2012,24(2):353−364. [doi: 10.1109/TKDE.2010.226]
[16] Liu LG, Sun H, Jia HL, Zhang Y. CGIM: Classificatory group index method for efficient ranked search of encrypted cloud data.
Chinese Journal of Electronics, 2019,47(2):331−336 (in Chinese with English abstract). [doi: 10.3969/j.issn.0372-2112.2019.02.
011]
[17] Huang H, Li XY, Sun Y, Huang LS. PPS: Privacy-preserving strategyproof social-efficient spectrum auction mechanisms. IEEE
Trans. on Parallel and Distributed Systems, 2015,26(5):1393−1404. [doi: 10.1109/TPDS.2014.2315200]
[18] Li MJ, Juan JST, Tsai JHC. Practical electronic auction scheme with strong anonymity and bidding privacy. Information Sciences,
2011,181(12):2576−2586. [doi: 10.1016/j.ins.2011.02.005]
[19] Yin X, Tian YL, Wang HL. Delegation auction scheme for big data pricing. Chinese Journal of Electronics, 2018,46(5):1113−1120
(in Chinese with English abstract). [doi: 10.3969/j.issn.0372-2112.2018.05.014]
[20] Chrétien S, Zhen WOH. Incoherent submatrix selection via approximate independence sets in scalar product graphs. In: Nicosia G,
Pardalos P, Umeton R, Giuffrida G, Sciacca V, eds. Proc. of the Machine Learning, Optimization, and Data Science. Cham:
Springer-Verlag, 2019. 95−105. https://doi.org/10.1007/978-3-030-37599-7_9
[21] Hofmann J, Fey D, Riedmann M, Eitzinger J, Hager G, Wellein G. Performance analysis of the Kahan-enhanced scalar product on
current multi-core and many-core processors. Concurrency and Computation: Practice and Experience, 2017,29(9):1−16. [doi: 10.
1002/cpe.3921]
[22] Lin WP, Wang K, Zhang ZL, Chen H. Revisiting security risks of asymmetric scalar product preserving encryption and its variants.
In: Proc. of the 2017 IEEE 37th Int’l Conf. on Distributed Computing Systems (ICDCS). Atlanta: IEEE, 2017. 1116−1125. [doi:
10.1109/ICDCS.2017.20]
[23] You YP, Li XH. Ordering scalar products with applications in financial engineering and actuarial science. Journal of Applied
Probability, 2016,53(1):47−56.
[24] Zhou SF, Dou JW, Guo YM, Mao Q, Li SD. Secure multiparty vector computation. Chinese Journal of Computers, 2017,40(5):
1134−1150 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2017.01134]
[25] Siabi B, Berenjkoub M, Susilo W. Optimally efficient secure scalar product with applications in cloud computing. IEEE Access,
2019,7:42798−42815. [doi: 10.1109/ACCESS.2019.2908230]
[26] Rong H, Wang HM, Huang K, Liu J, Xian M. Privacy-preserving scalar product computation in cloud environments under multiple
keys. In: Yin H, ed. Proc. of the Intelligent Data Engineering and Automated Learning. Cham: Springer-Verlag, 2016. 248−258.
https://doi.org/10.1007/978-3-319-46257-8_27