Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 315-326.doi: 10.11896/jsjkx.221000053

• Information Security • Previous Articles     Next Articles

Utility-optimized Local Differential Privacy Joint Distribution Estimation Mechanisms

YIN Shiyu, ZHU Youwen, ZHANG Yue   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2022-10-09 Revised:2023-02-15 Online:2023-10-10 Published:2023-10-10
  • About author:YIN Shiyu,born in 1998,postgraduate,is a student member of China Computer Federation.Her main research interests include data security and differential privacy.ZHU Youwen,born in 1986,Ph.D,professor,is a member of China Computer Federation.His main research interests include data security,privacy computing,and applied cryptography.
  • Supported by:
    National Key Research and Development Program of China(2020YFB1005900),National Natural Science Foundation of China(62172216),Natural Science Foundation of Jiangsu Province,China(BK20211180) and Guangxi Key Laboratory of Cryptography and Information Security(GCIS202107).

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Abstract: Compared with traditional centralized differential privacy,local differential privacy(LDP) has the advantage of not re-lying on trusted third parties,but it also has the problem of low data utility.The utility-optimized local differential privacy(ULDP) can improve the accuracy of estimation results by taking advantage of the sensitivity differences of different inputs.Two-dimensional data joint distribution calculation can be widely used in many data analysis scenarios.However,how to realize two-dimensional data joint distribution estimation under the ULDP model is still an important problem that has not yet been solved.Aiming at this problem,the definition of the two-dimensional ULDP model is given first,taking into account the four cases of whether the two attributes are sensitive or not.Secondly,under this model,for the joint distribution estimation problem,two mechanisms joint utility-optimized randomized response(JuRR) and cartesian product randomized response(CPRR) are proposed,and the unbiasedness of the estimation results is proved theoretically.Finally,comparative experiments are carried out on real datasets to discuss the influence of different parameters on the estimation error.Experimental results show that the proposed two mechanisms have better data utility.

Key words: Local differential privacy, Utility-optimized, Joint distribution, Frequency estimation, Difference in sensitivity

CLC Number: 

  • TP309
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