skip to main content
10.1145/3573428.3573784acmotherconferencesArticle/Chapter ViewAbstractPublication PageseitceConference Proceedingsconference-collections
research-article

Study of a two-level variable model optimization algorithm based on differential privacy

Published: 15 March 2023 Publication History

Abstract

Traditional ADMM methods suffer from slow convergence and low recognition when faced with group-structured data. This paper therefore uses the parallel alternating directional multiplier method (PADMM) algorithm based on a two-layer penalty variable model with cross-features to solve the model, and uses an optimisation algorithm with normalised weight decay Nadam to improve the convergence speed of the model. Considering the risk of privacy leakage when the data is iterated, a moderate amount of noise is added to the model solving process by perturbing the output of the algorithm to achieve privacy protection of the data. Experiments show that for the same privacy budget, the DP-Nadam with weight attenuation is more accurate than the DP-Nadam in terms of model accuracy as the number of training rounds increases, and the loss is also less than that of the traditional DP-Nadam.

References

[1]
Nguyen V C, Ng C T. Variable selection under multicollinearity using modified log penalty[J]. Journal of Applied Statistics, 2020, 47(2): 201-230.
[2]
Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(05): 571-588.
[3]
Mahanan W, Chaovalitwongse W, Natwichai J. Data privacy preservation algorithm with k-anonymity[J]. World Wide Web, 2021, 24(5): 1551-1561.
[4]
Dhany H W, Izhari F, Fahmi H, Encryption and decryption using password based encryption, MD5, and DES[C]//International Conference on Public Policy, Social Computing and Development 2017 (ICOPOSDev 2017). Atlantis Press, 2017: 278-283.
[5]
Dwork C. Differential privacy [C]//Proceedings of the 33rd International Colloquium on Automata, Languages and Programming. Venice, Italy, 2006, 1-12.
[6]
Alvim M S, Andrés M E, Chatzikokolakis K, Differential privacy: on the trade-off between utility and information leakage[C]//International Workshop on Formal Aspects in Security and Trust. Springer, Berlin, Heidelberg, 2011: 39-54.
[7]
Dwork C. A firm foundation for private data analysis[J]. Communications of the ACM, 2011, 54(1): 86-95.
[8]
Ebadi H, Sands D, Schneider G. Differential privacy: Now it's getting personal[J]. Acm Sigplan Notices, 2015, 50(1): 69-81.
[9]
Zhang H, Roth E, Haeberlen A, Fuzzi: A three-level logic for differential privacy[J]. Proceedings of the ACM on Programming Languages, 2019, 3(ICFP): 1-28.
[10]
Kurz C. Understanding differential privacy[J]. Significance, 2021, 18(3): 24-27.
[11]
Ma T, Song F. A trajectory privacy protection method based on random sampling differential privacy[J]. ISPRS International Journal of Geo-Information, 2021, 10(7): 454.
[12]
Liu F. Generalized gaussian mechanism for differential privacy[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(4): 747-756.
[13]
Dong J, Roth A, Su W J. Gaussian differential privacy[J]. Journal of the Royal Statistical Society Series B, 2022, 84(1): 3-37.
[14]
Abadi M, Chu A, Goodfellow I, Deep learning with differential privacy[C]//Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016: 308-318.
[15]
Jones L A, Champ C W, Rigdon S E. The performance of exponentially weighted moving average charts with estimated parameters[J]. Technometrics, 2001, 43(2): 156-167.
[16]
Wang J, Cao Z. Chinese text sentiment analysis using LSTM network based on L2 and Nadam [C]//2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE, 2017: 1891-1895.
[17]
Tiulpin A, Thevenot J, Rahtu E, Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach[J]. Scientific reports, 2018, 8(1): 1-10.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Nadam
  2. Privacy protection
  3. Weight Decay

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

EITCE 2022

Acceptance Rates

Overall Acceptance Rate 508 of 972 submissions, 52%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 17
    Total Downloads
  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media