Page 235 - 《软件学报》2021年第11期
P. 235

欧阳佳  等:面向频繁项集挖掘的本地差分隐私事务数据收集方法                                                  3561


                [16]    Ouyang J, Yin J, Liu SP. Differential privacy publishing strategy for distributed transaction data. Ruan Jian Xue Bao/Journal of
                     Software, 2015,26(6):1457−1472 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4576.htm [doi: 10.13328/j.
                     cnki.jos.004576]
                [17]    Su S, Xu S, Cheng X, et al. Differentially private frequent itemset mining via transaction splitting. IEEE Trans. on Knowledge and
                     Data Engineering, 2015,27(7):1875−1891.
                [18]    Li N, Qardaji  WH,  Su D,  et  al. PrivBasis: Frequent itemset  mining  with differential privacy. Proc. of the  VLDB  Endowment,
                     2012,5(11):1340−1351.
                [19]    Lee J, Clifton C. Top-k frequent itemsets via differentially private FP-trees. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on
                     Knowledge Discovery and Data Mining. New York: ACM, 2014. 931−940.
                [20]    Xiong X, Chen F, Huang P, et al. Frequent itemsets mining with differential privacy over large-scale data. IEEE Access, 2018,6:
                     28877−28889.
                [21]    Fanti  G, Pihur  V, Erlingsson  U.  Building  a  RAPPOR with the unknown: Privacy-preserving learning of  associations  and data
                     dictionaries. Proc. on Privacy Enhancing Technologies, 2016, 2016(3):41−61.
                [22]    Erlingsson Ú, Pihur V, Korolova A. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In: Proc. of the 2014
                     ACM SIGSAC Conf. on Computer and Communications Security (CCS 2014). New York: ACM, 2014. 1054−1067.
                [23]    Warner SL.  Randomized response: A survey  technique  for  eliminating  evasive  answer bias. Journal of the  American Statistical
                     Association, 1965,60(309):63−69.
                [24]    Sun C, Fu Y, Zhou J, et al. Personalized privacy-preserving frequent itemset mining using randomized response. The Scientific
                     World Journal, 2014,2014:Article ID 686151. http://dx.doi.org/10.1155/2014/686151
                [25]    Evfimievski  A,  Srikant R, Agrawal R,  et  al. Privacy  preserving  mining of  association  rules. Information Systems, 2004,29(4):
                     343−364.
                [26]    Ding  B, Kulkarni  J,  Yekhanin S. Collecting telemetry  data privately. In: Proc. of the  Neural Information Processing Systems.
                     Curran Associates, 2017. 3574−3583.
                [27]    Kairouz P, Bonawitz  K,  Ramage  D.  Discrete distribution  estimation under local privacy. In: Proc. of the  33rd International
                     Conference on International Conference on Machine Learning, 2016, 48:5662−5676.
                [28]    Duchi JC, Jordan MI, Wainwright MJ. Local privacy and minimax bounds: Sharp rates for probability estimation. In: Proc. of the
                     27th Annual Conf. on Neural Information Processing Systems. Nevada: Curran Associates, 2013. 1529−1537.
                [29]    Andrés M, Bordenabe N, Chatzikokolakis K, et al. Geo-indistinguishability: Differential privacy for location-based systems. In:
                     Proc. of the 2013 ACM SIGSAC Conf. on Computer & Communications Security. New York: ACM, 2013. 901−914.
                [30]    Bordenabe N, Chatzikokolakis K, Palamidessi C. Optimal geo-indistinguishable mechanisms for location privacy. In: Proc. of the
                     2014 ACM SIGSAC Conf. on Computer and Communications Security. New York: ACM, 2014. 251−262.
                [31]    Hsu J, Khanna S, Roth A. Distributed private heavy hitters. In: Proc. of the 39th international colloquium conference on Automata,
                     Languages, and Programming. New York: ACM, 2012. 461−472.
                [32]    Bassily R, Smith A. Local, private, efficient protocols for succinct histograms. New York: ACM, 2015. 127−135.
                [33]    Zhan Q, Yin Y, Ting Y, et al. Heavy hitter estimation over set-valued data with local differential privacy. In: Proc. of the Computer
                     and Communications Security. New York: ACM, 2016. 192−203.
                [34]    Mehmet Emre G, Acar T, Stacey T, et al. Secure and utility-aware data collection with condensed local differential privacy. arXiv
                     preprint arXiv:1905.06361, 2019.
                [35]    Lee J, Clifton C. Differential identifiability. In: Proc. of the 18th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data
                     Mining. New York: ACM, 2012. 1041−1049.
                [36]    Ouyang J, Xiao ZH, Liu SP, et al. Heuristic privacy parameter setting strategy for differential privacy model. Application Research
                     of Computers, 2019,36(1):250−253 (in Chinese with English abstract).
                [37]    Evfimievski  A,  Gehrke J, Srikant  R. Limiting privacy  breaches in privacy preserving data  mining. In: Proc.  of the 22th  ACM
                     SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems. New York: ACM, 2003. 211−222.
                [38]    Wang W, Carreira-Perpiñán MÁ.  Projection  onto  the probability  simplex: An efficient algorithm with a  simple  proof, and an
                     application. arXiv preprint arXiv:1309.1541, 2013.
   230   231   232   233   234   235   236   237   238   239   240