Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 372-378.doi: 10.11896/jsjkx.240700128

• Information Security • Previous Articles     Next Articles

Happiness Prediction Approach via Adaptive Privacy Budget Allocation

LUO Yanjie1, LI Lin1, WU Xiaohua1, LIU Jia2,3   

  1. 1 School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
    2 Wuhan Library of Chinese Academy of Science, Wuhan 430071, China
    3 Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China
  • Received:2024-07-22 Revised:2024-09-18 Published:2025-07-17
  • About author:LUO Yanjie,born in 2000, postgra- duate.Her main research interests include data mining and machine lear-ning.
    LI Lin,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.34840M).Her main research interests include multi-modal machine learning,information retrieval and re- commender systems.
  • Supported by:
    National Natural Science Foundation of China(62276196) and Hubei Key Laboratory of Big Data in Science and Technology Open Topic(E3KF291001).

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Abstract: Happiness prediction aims to forecast individuals' life satisfaction and happiness indices by analyzing data.Online platforms for happiness prediction possess a vast amount of data,which also carries the risk of privacy breaches.Existing differential privacy machine learning methods overlook the privacy needs of different attributes.Moreover,privacy budget averaging approach injects excessive noise into the model,leading to performance degradation.To address these issues,this paper proposes a method called Adaptive Privacy Budget Allocation for Happiness Prediction(APBA-DP).Initially,attributes are graded based on users' privacy preferences,and privacy budgets are allocated using information entropy.Subsequently,happiness prediction model establishes an attribute mapping layer to ensure personalised privacy protection.Experimental results on ESS and CGSS datasets show that the accuracy of APBA-DP algorithm is improved by 2.3%~4.4% compared with the traditional differential privacy algorithms under certain privacy protection intensity.At the same time,the success rate of member inference attacks is reduced by 14.7% and 12.5% on average compared with the model without differential privacy protection.

Key words: Happiness prediction, Differential privacy, Privacy budget

CLC Number: 

  • TP391
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