Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

Pei Feng; Haiping Zhu; Yu Liu; Yan Chen; Qinghua Zheng

doi:10.1109/ICEBE.2018.00054

Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

Pei Feng
, Haiping Zhu
, Yu Liu
, Yan Chen
, Qinghua Zheng

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this paper proposes a differential privacy collaborative filtering recommendation algorithm based on learner behavior similarity. By adding noise obeying the Laplace distribution to the learner behavior similarity matrix, the recommendation accuracy rate does not reduce, as well as the privacy of student is protected effectively.

Original language	English
Title of host publication	Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	285-288
Number of pages	4
ISBN (Electronic)	9781538679920
DOIs	https://doi.org/10.1109/ICEBE.2018.00054
State	Published - 27 Dec 2018
Event	15th International Conference on e-Business Engineering, ICEBE 2018 - Xi'an, China Duration: 12 Oct 2018 → 14 Oct 2018

Publication series

Name	Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018

Conference

Conference	15th International Conference on e-Business Engineering, ICEBE 2018
Country/Territory	China
City	Xi'an
Period	12/10/18 → 14/10/18

Keywords

Collaborative filtering
Differential privacy
Learner behavior similarity

Access to Document

10.1109/ICEBE.2018.00054

Cite this

Feng, P., Zhu, H., Liu, Y., Chen, Y., & Zheng, Q. (2018). Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior. In Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018 (pp. 285-288). Article 8592665 (Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEBE.2018.00054

Feng, Pei ; Zhu, Haiping ; Liu, Yu et al. / Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior. Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 285-288 (Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018).

@inproceedings{a4d02f7669df4bb89a26bf4bd4407a7e,

title = "Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior",

abstract = "Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this paper proposes a differential privacy collaborative filtering recommendation algorithm based on learner behavior similarity. By adding noise obeying the Laplace distribution to the learner behavior similarity matrix, the recommendation accuracy rate does not reduce, as well as the privacy of student is protected effectively.",

keywords = "Collaborative filtering, Differential privacy, Learner behavior similarity",

author = "Pei Feng and Haiping Zhu and Yu Liu and Yan Chen and Qinghua Zheng",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th International Conference on e-Business Engineering, ICEBE 2018 ; Conference date: 12-10-2018 Through 14-10-2018",

year = "2018",

month = dec,

day = "27",

doi = "10.1109/ICEBE.2018.00054",

language = "英语",

series = "Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "285--288",

booktitle = "Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018",

}

Feng, P, Zhu, H, Liu, Y, Chen, Y & Zheng, Q 2018, Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior. in Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018., 8592665, Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 285-288, 15th International Conference on e-Business Engineering, ICEBE 2018, Xi'an, China, 12/10/18. https://doi.org/10.1109/ICEBE.2018.00054

Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior. / Feng, Pei; Zhu, Haiping; Liu, Yu et al.
Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 285-288 8592665 (Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

AU - Feng, Pei

AU - Zhu, Haiping

AU - Liu, Yu

AU - Chen, Yan

AU - Zheng, Qinghua

PY - 2018/12/27

Y1 - 2018/12/27

N2 - Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this paper proposes a differential privacy collaborative filtering recommendation algorithm based on learner behavior similarity. By adding noise obeying the Laplace distribution to the learner behavior similarity matrix, the recommendation accuracy rate does not reduce, as well as the privacy of student is protected effectively.

AB - Traditional collaborative filtering recommendation algorithm based on learning resources use a large amount of student personal information and behavior information. This will put the user's privacy at risks since that students' information can be mined by analyzing the recommendation results. Considering that differential privacy theory can effectively protect user privacy through strict mathematical definition and maximum background knowledge assumptions, this paper proposes a differential privacy collaborative filtering recommendation algorithm based on learner behavior similarity. By adding noise obeying the Laplace distribution to the learner behavior similarity matrix, the recommendation accuracy rate does not reduce, as well as the privacy of student is protected effectively.

KW - Collaborative filtering

KW - Differential privacy

KW - Learner behavior similarity

UR - https://www.scopus.com/pages/publications/85061510700

U2 - 10.1109/ICEBE.2018.00054

DO - 10.1109/ICEBE.2018.00054

M3 - 会议稿件

AN - SCOPUS:85061510700

T3 - Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018

SP - 285

EP - 288

BT - Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on e-Business Engineering, ICEBE 2018

Y2 - 12 October 2018 through 14 October 2018

ER -

Feng P, Zhu H, Liu Y, Chen Y , Zheng Q. Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior. In Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 285-288. 8592665. (Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018). doi: 10.1109/ICEBE.2018.00054

Differential Privacy Protection Recommendation Algorithm Based on Student Learning Behavior

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