高效的决策树隐私分类服务协议

doi:10.11959/j.issn.1000-436x.2021149

Abstract

Abstract:

To provide privacy-preserving decision tree classification services in the Internet of things (IoT) big data scenario, an efficient privacy-preserving decision tree classification protocol was proposed by adopting the secure multiparty computation framework into the classification model.The entire protocol consisted of three parts: the original decision tree model mixing, the Boolean share-based privacy-preserving comparing, and the 1-out-of-n oblivious transfer-based classification result obtaining.Via the proposed protocol, the service providers could protect the parameters of their decision tree models and the users were able to derive the classification result without exposing their privately hold data.Through a concrete security analysis, the proposed protocol was proved to be secure against semi-honest adversaries.By implementing the proposed protocol on various practical decision tree models from open datasets, the classification accuracy and the average time cost for completing one privacy-preserving classification service were evaluated.After compared with existing related works, the performance superiority of the proposed protocol is demonstrated.

Key words: decision tree, privacy preserving, oblivious transfer, secure multiparty computation

CLC Number:

TN92

Lichuan MA, Jiayi PENG, Qingqi PEI, Haojin ZHU. Efficient privacy-preserving decision tree classification protocol[J]. Journal on Communications, 2021, 42(8): 80-89.

Figures/Tables 6

参数	含义
S	拥有决策树分类模型的云服务器
C	请求决策树隐私分类的用户
T	决策树分类模型
$x$	用户数据
z_x	$x$ 所对应的分类结果
$I : V \to [1, \dots, d]$	标记函数
$I V_{0}$	原始决策树分类模型内部节点标号序列
$I X_{0}$	与 $I V_{0}$ 对应的属性标号序列
$W_{0}$	与 $I V_{0}$ 对应的内部节点阈值序列
δ_r	定义在 $I V_{0}$ 上的随机置换
$L V$	$I V_{0}$ 经过函数δ_r作用后的随机置换
$L X$	与 $I V_{r}$ 对应的属性标号序列
$W_{r}$	与 $I V_{r}$ 对应的内部节点阈值序列
[x]	x的二进制表示
$[x]_{S}, [x]_{C}$	[x]的布尔共享，且 $[x] = [x]_{S} \oplus [x]_{C}$
⊕	“异或”运算
?	“与”运算
$(_{1}^{n}) O T_{λ}$	1-out-of-n不经意传输过程

References 26

[1]	NESHENKO N , BOU-HARB E , CRICHIGNO J ,et al. Demystifying IoT security:an exhaustive survey on IoT vulnerabilities and a first empirical look on Internet-scale IoT exploitations[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(3): 2702-2733.
[2]	CVITI? I , PERAKOVI? D ， PERI? ,et al. Novel approach for detection of IoT generated DDoS traffic[J]. Wireless Networks, 2021,27(3): 1573-1586.
[3]	MOHAMMADI M , AL-FUQAHA A , SOROUR S ,et al. Deep learning for IoT big data and streaming analytics:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(4): 2923-2960.
[4]	陈立南, 刘阳, 马严 ,等. 基于统计的高效决策树分组分类算法[J]. 通信学报, 2014,35(Z1): 58-64.
	CHEN L N , LIU Y , MA Y ,et al. Efficient-cutting packet classification algorithm based on the statistical decision tree[J]. Journal on Communications, 2014,35(Z1): 58-64.
[5]	LI T , LI X , ZHONG X Y ,et al. Communication-efficient outsourced privacy-preserving classification service using trusted processor[J]. Information Sciences, 2019,505: 473-486.
[6]	PINHEIRO A J , DE M BEZERRA J , BURGARDT C A P ,et al. Identifying IoT devices and events based on packet length from encrypted traffic[J]. Computer Communications, 2019,144: 8-17.
[7]	STALLINGS W . Handling of personal information and deidentified,aggregated,and pseudonymized information under the California consumer privacy act[J]. IEEE Security ＆ Privacy, 2020,18(1): 61-64.
[8]	SHASTRI S , WASSERMAN M , CHIDAMBARAM V . GDPR anti-patterns[J]. Communications of the ACM, 2021,64(2): 59-65.
[9]	QI A M , SHAO G S , ZHENG W T . Assessing China's cybersecurity law[J]. Computer Law ＆ Security Review, 2018,34(6): 1342-1354.
[10]	LIANG J W , QIN Z , XIAO S ,et al. Efficient and secure decision tree classification for cloud-assisted online diagnosis services[J]. IEEE Transactions on Dependable and Secure Computing, 2021,18(4): 1632-1644.
[11]	BOST R , POPA R A , TU S ,et al. Machine learning classification over encrypted data[C]// Proceedings 2015 Network and Distributed System Security Symposium. VA:Internet Society, 2015: 4324-4325.
[12]	WU D J , FENG T , NAEHRIG M ,et al. Privately evaluating decision trees and random forests[J]. Proceedings on Privacy Enhancing Technologies, 2016,2016(4): 335-355.
[13]	TUENO A , KERSCHBAUM F , KATZENBEISSER S . Private evaluation of decision trees using sublinear cost[J]. Proceedings on Privacy Enhancing Technologies, 2019,2019(1): 266-286.
[14]	刘琳岚, 高声荣, 舒坚 . 基于随机森林的链路质量预测[J]. 通信学报, 2019,40(4): 202-211.
	LIU L L , GAO S R , SHU J . Link quality prediction based on random forest[J]. Journal on Communications, 2019,40(4): 202-211.
[15]	CHEN X , ZHU C C , YIN J . Ensemble of decision tree reveals potential miRNA-disease associations[J]. PLoS Computational Biology, 2019,15(7): 1-24.
[16]	李佳, 云晓春, 李书豪 ,等. 基于混合结构深度神经网络的 HTTP恶意流量检测方法[J]. 通信学报, 2019,40(1): 24-33.
	LI J , YUN X C , LI S H ,et al. HTTP malicious traffic detection method based on hybrid structure deep neural network[J]. Journal on Communications, 2019,40(1): 24-33.
[17]	LI F H , LI H , NIU B ,et al. Privacy computing:concept,computing framework,and future development trends[J]. Engineering, 2019,5(6): 1179-1192.
[18]	RAN C . Security and composition of multiparty cryptographic protocols[J]. Journal of Cryptology, 2000,13(1): 143-202.
[19]	BRAKERSKI Z , GENTRY C , VAIKUNTANATHAN V . Fully homomorphic encryption without bootstrapping[J]. ACM Transactions on Computation Theory, 2014,6(3): 1-36.
[20]	DAMGARD I , GEISLERR M , KROIGAARD M . Efficient and secure comparison for on-line auctions[C]// 2007Australasian conference on information security and privacy. Berlin:Springer, 2007: 416-430.
[21]	ERKIN Z , FRANZ M , GUAJARDO J ,et al. Privacy-preserving face recognition[C]// International Symposium on Privacy Enhancing Technologies Symposium. Berlin:Springer, 2009: 235-253.
[22]	GOLDREICH O , MICALI S , WIGDERSON A . How to play any mental game,or a completeness theorem for protocols with honest majority[C]// Proceedings of the 19th Symposium on Theory of Computing. New York:ACM Press, 2019: 307-328.
[23]	KOLESNIKOV V , SADEGHI A R , SCHNEIDER T . Improved garbled circuit building blocks and applications to auctions and computing minima[C]// International Conference on Cryptology and Network Security. Berlin:Springer, 2009: 1-20.
[24]	BEAVER D , . Efficient multiparty protocols using circuit randomization[C]// Annual International Cryptology Conference. Berlin:Springer, 1991: 420-432.
[25]	NAOR M , PINKAS B . Computationally secure oblivious transfer[J]. Journal of Cryptology, 2005,18(1): 1-35.
[26]	ASHAROV G , LINDELL Y , SCHNEIDER T ,et al. More efficient oblivious transfer and extensions for faster secure computation[C]// 2013 ACM SIGSAC conference on Computer ＆ communications security. New York:ACM Press, 2013: 535-548.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	分类准确率
数据集	明文状态	本文协议
Breast-cancer	0.894	0.894
Nursery	0.936	0.936
ECG	0.886	0.886
Credit-screening	0.877	0.877
Housing	1.000	1.000
Spambase	0.990	0.990

Efficient privacy-preserving decision tree classification protocol

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 26

Related Articles 15

Metrics

Recommended 0

[1]	Sheng GAO, Kang XIANG, Youliang TIAN, Weijie TAN, Tao FENG, Xiaoxue WU. BCP-based joint delegation learning model and protocol [J]. Journal on Communications, 2021, 42(5): 137-148.
[2]	Hongfa DING,Changgen PENG,Youliang TIAN,Shuwen XIANG. Privacy risk adaptive access control model via evolutionary game [J]. Journal on Communications, 2019, 40(12): 9-20.
[3]	Yong ZENG,Lingjie ZHOU,Zhongyuan JIANG,Zhihong LIU,Jianfeng MA. Security analysis of weighted network anonymity based on singular value decomposition [J]. Journal on Communications, 2018, 39(5): 23-33.
[4]	Hui LIN,Mengyang YU,Youliang TIAN,Yijie HUANG. Dynamic game and reliable recommendation based transferring reputation mechanism for mobile cloud computing [J]. Journal on Communications, 2018, 39(5): 85-93.
[5]	Lingbo WEI,Xiaobing FENG,Chi ZHANG,Hualong SHENG,Nenghai YU. Network function outsourcing system based on prefix-preserving encryption [J]. Journal on Communications, 2018, 39(4): 159-166.
[6]	Yihan YU,Yu FU,Xiaoping WU. Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework [J]. Journal on Communications, 2018, 39(1): 70-77.
[7]	Zhi-qiang WU,Ken-li LI,Hui ZHENG. Efficient and scalable architecture for searchable symmetric encryption [J]. Journal on Communications, 2017, 38(8): 79-93.
[8]	Hao WANG,Zheng-quan XU,Li-zhi XIONG,Tao WANG. CLM:differential privacy protection method for trajectory publishing [J]. Journal on Communications, 2017, 38(6): 85-96.
[9]	Wen-jun XIONG,Zheng-quan XU,Hao WANG. Privacy level evaluation of differential privacy for time series based on filtering theory [J]. Journal on Communications, 2017, 38(5): 172-181.
[10]	Hong-cheng LI,Xiao-ping WU,Yan CHEN. k-means clustering method preserving differential privacy in MapReduce framework [J]. Journal on Communications, 2016, 37(2): 125-131.
[11]	Li-bing WU,Yong XIE,Yu-bo ZHANG. Efficient and secure message authentication scheme for VANET [J]. Journal on Communications, 2016, 37(11): 1-10.
[12]	Yong-hao GU,Jiu-chuan LIN,Da GUO. Clustering-based dynamic privacy preserving method for social networks [J]. Journal on Communications, 2015, 36(Z1): 126-130.
[13]	Wei-ni ZENG,Ya-ping LIN,Ye-qing YI,Shi-ming HE,Peng CHEN. Data processing based on the privacy-preserving vector for wireless sensor networks [J]. Journal on Communications, 2015, 36(9): 34-46.
[14]	Li-hui LAN,Shi-guang JU. Privacy preserving based on differential privacy for weighted social networks [J]. Journal on Communications, 2015, 36(9): 145-159.
[15]	Chao WANG,Jing YANG,Jian-pei ZHANG,Gang LV. Algorithm for k-anonymity based on projection area density partition [J]. Journal on Communications, 2015, 36(8): 125-134.