基于Tangle網(wǎng)絡(luò)的移動(dòng)群智感知數(shù)據(jù)安全交付模型

趙國生; 張慧; 王健

doi:10.11999/JEIT190370

基于Tangle網(wǎng)絡(luò)的移動(dòng)群智感知數(shù)據(jù)安全交付模型

doi: 10.11999/JEIT190370

趙國生¹,
張慧^1, ,,
王健²

1.
哈爾濱師范大學(xué)計(jì)算機(jī)科學(xué)與信息工程學(xué)院哈爾濱 150025
2.
哈爾濱理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院哈爾濱 150080

基金項(xiàng)目: 國家自然科學(xué)基金(61202458, 61403109)，黑龍江自然科學(xué)基金(F2017021)，哈爾濱市科技創(chuàng)新人才研究專項(xiàng)資金(2016RAQXJ036)

詳細(xì)信息

作者簡介:
趙國生：男，1977年生，博士，教授，研究方向?yàn)榭缮婕夹g(shù)、認(rèn)知網(wǎng)絡(luò)、可信計(jì)算

張慧：女，1994年生，碩士生，研究方向?yàn)槿褐歉兄?/p>
王?。号?979年生，博士，教授，研究方向?yàn)镾DN、可生存技術(shù)、認(rèn)知網(wǎng)絡(luò)、群智感知

通訊作者:
張慧　18746424159@163.com

中圖分類號(hào): TP309
計(jì)量
- 文章訪問數(shù): 2928
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-05-23
- 修回日期: 2019-09-03
- 網(wǎng)絡(luò)出版日期: 2019-09-17
- 刊出日期: 2020-06-04

A Mobile Crowdsensing Data Security Delivery Model Based on Tangle Network

1.
College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
2.
School of Computer Science and Technology, Harbin University of Scienceand Technology, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61202458, 61403109), The Natural Science Foundation of Heilongjiang Province (F2017021), The Harbin Science and Technology Innovation Research Funds (2016RAQXJ036)

摘要

摘要:
針對(duì)現(xiàn)有群智感知平臺(tái)在數(shù)據(jù)和酬金交付過程中存在的安全風(fēng)險(xiǎn)和隱私泄露問題，該文提出一種基于Tangle網(wǎng)絡(luò)的分布式群智感知數(shù)據(jù)安全交付模型。首先，在數(shù)據(jù)感知階段，調(diào)用局部異常因子檢測(cè)算法剔除異常數(shù)據(jù)，聚類獲取感知數(shù)據(jù)并確定可信參與者節(jié)點(diǎn)。然后，在交易寫入階段，使用馬爾科夫蒙特卡洛算法選擇交易并驗(yàn)證其合法性，通過注冊(cè)認(rèn)證中心登記完成匿名身份數(shù)據(jù)上傳，并將交易同步寫入分布式賬本。最后，結(jié)合Tangle網(wǎng)絡(luò)的累計(jì)權(quán)重共識(shí)機(jī)制，當(dāng)交易安全性達(dá)到閾值時(shí)，任務(wù)發(fā)布者可進(jìn)行數(shù)據(jù)和酬金的安全交付。仿真試驗(yàn)表明，在模型保護(hù)用戶隱私的同時(shí)，增強(qiáng)了數(shù)據(jù)和酬金的安全交付能力，相比現(xiàn)有感知平臺(tái)降低了時(shí)間復(fù)雜度和任務(wù)發(fā)布成本。
- 移動(dòng)群智感知 /
- Tangle網(wǎng)絡(luò) /
- 感知數(shù)據(jù) /
- 安全交付
Abstract:
Considering the security risks and privacy leaks in the process of data and reward in the Mobile CrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed. Firstly, in the data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data, cluster the perception data and determine the trusted participant. Then, in the transaction writing stage, Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymous identity data is uploaded by registering with the authentication center, and the transaction is synchronously written to the distributed account book. Finally, combined with Tangle network cumulative weight consensus mechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data and rewards. The simulation results show that the model not only protects user privacy, but also enhances the ability of secure delivery of data and reward. Compared with the existing sensing platform, the model reduces the time complexity and task publishing cost.
- Mobile CrowdSensing (MCS) /
- Tangle network /
- Perceived data /
- Secure delivery

HTML全文

圖 1 基于Tangle網(wǎng)絡(luò)的感知數(shù)據(jù)交付模型

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圖 2 Tangle網(wǎng)絡(luò)交易結(jié)構(gòu)

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圖 3 交易結(jié)構(gòu)

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圖 4 身份匿名過程

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圖 5 隱私數(shù)據(jù)泄露的概率

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圖 6 時(shí)間復(fù)雜性分析

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圖 7 TNM模型與AMT機(jī)制服務(wù)費(fèi)對(duì)比

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表 1 算法1：基于參與者選擇的LOF算法

　輸入：參與者的位置信息集N, k近鄰參數(shù)

　輸出：前k個(gè)數(shù)據(jù)的LOF

　(1) 計(jì)算任意數(shù)據(jù)點(diǎn)之間的歐式距離${\rm{disk}}(i,j)$;

　(2) 計(jì)算所有數(shù)據(jù)點(diǎn)和其前k個(gè)數(shù)據(jù)點(diǎn)間的距離${\rm{disk}}_k^{}(i)$;

　(3) 計(jì)算所有數(shù)據(jù)點(diǎn)的k距離鄰居${N_K}(i)$; $ {N_K}(i) = \left\{ {\left. {i'} \right|} \right.i' \in N, $
$ \left.{\rm{dist}}(i,i') \le {\rm{dis}}{{\rm{t}}_k}(i) \right\}$

　(4) 計(jì)算所有數(shù)據(jù)點(diǎn)的局部可達(dá)密度${\rm{lr}}{{\rmq7j3ldu95}_k}(i)$：
　　$\begin{array}{*{20}{l} }\quad\quad { {\rm{lr} }{ {\rmq7j3ldu95 }_k}(i) = \frac{ {\left\| {\left. { {N_K}(i)} \right\|} \right.} }{ {\displaystyle\sum\limits_{i' \in {N_k}(i)} { {\rm{reachdis} }{ {\rm{t} }_k}(i' \leftarrow i)} } } }\\\qquad { {\rm{reachdis} }{ {\rm{t} }_k}(i' \leftarrow i) = {\rm{max} }\left. {\left\{ { {\rm{dis} }{ {\rm{t} }_k}(i),{\rm{dist} }(i,i')} \right.} \right\} }\end{array}\;\;\;\;\;\;\;\;\quad\ \ \left( 1 \right)$

　(5) 計(jì)算${\rm{LO}}{{\rm{F}}_K}(i)$
　　$\begin{array}{*{20}{l}}\quad\quad\ \ {{\rm{LO}}{{\rm{F}}_K}(i) = \frac{{\displaystyle\sum\limits_{i' \in {N_K}(i)} {\frac{{{\rm{lr}}{{\rmq7j3ldu95}_k}(i')}}{{{\rm{lr}}{{\rmq7j3ldu95}_k}(i)}}} }}{{\left\| {\left. {{N_K}(i)} \right\|} \right.}} }\\\quad\quad \quad = {\displaystyle\sum\limits_{i' \in {N_K}(i)} {{\rm{lr}}{{\rmq7j3ldu95}_k}(i') \cdot \sum\limits_{i' \in {N_K}(i)} {{\rm{reachdis}}{{\rm{t}}_k}(i' \leftarrow i)} } }\;\;\;\;\;\;\;\;\;\;\;\;\left( 2 \right)\end{array}$

　(6) 對(duì)${\rm{LO}}{{\rm{F}}_K}(i)$進(jìn)行排序，剔除LOF高的數(shù)據(jù)。

下載: 導(dǎo)出CSV

表 2 算法2：基于MCMC的端點(diǎn)選擇算法

　輸入：馬爾可夫鏈狀態(tài)轉(zhuǎn)移矩陣Q，平穩(wěn)分布$\pi (x)$，最大轉(zhuǎn)移次數(shù)n1，選定時(shí)間間隔[W, 2W]及該間隔下的樣本個(gè)數(shù)n2(此時(shí)的樣本個(gè)數(shù)為
新到的交易所觀察到的交易數(shù)目)。

　輸出：兩個(gè)最先走到Tip的粒子為新交易將驗(yàn)證的端點(diǎn)。

　for t=0 to n1 + n2–1:

　(1) 初始化馬爾可夫鏈${X_0} = {x_0}$;

　(2) 獨(dú)立的在該選定的間隔中隨機(jī)放入N個(gè)粒子定義為“Walker”；

　(3) 每個(gè)粒子根據(jù)定義的轉(zhuǎn)移概率P隨機(jī)的選出一條路徑，向著Tip的方向進(jìn)行游走。其中轉(zhuǎn)移概率定義為：
　$\qquad{P_{xy} } = \dfrac{ { { {\rm e}^{ - a({H_x} - {H_y})} } } }{ {\displaystyle\sum\limits_{z:x \leftarrow z} { { {\rm e}^{ - a({H_x} - {H_z})} } } } }\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \left( 3 \right)$

　　　其中，$a > 0$，為自定義參數(shù)，${H_x}$和${H_y}$為交易x和交易y的累計(jì)權(quán)重，轉(zhuǎn)移后第t個(gè)時(shí)刻的馬爾可夫鏈狀態(tài)為${X_t} = {{{x}}_t}$，下一個(gè)交易可
能的狀態(tài)為${y_{t + 1}} = {x_t}p(x|{x_t})$，此時(shí)$\pi (x) = ({x_{n1} },{x_{n1 + 1} },···,{x_{n1 + n2 - 1} })$。

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表 3 群智感知過程中的隱私泄露點(diǎn)

隱私泄露過程	隱私泄露位置	竊取隱私難易程度
參與者將采集數(shù)據(jù)上傳至TS	參與者與TS通信的中間網(wǎng)絡(luò)遭受中間人攻擊	易
參與者與其他傳感器交互	傳感器設(shè)備	易
交易寫入Tangle網(wǎng)絡(luò)	Tangle網(wǎng)絡(luò)	易
TS調(diào)用LOF算法	TS	中
TS指定獲勝節(jié)點(diǎn)	TS	中
PS支付酬金	PS	中

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表 4 Tangle網(wǎng)絡(luò)處理數(shù)據(jù)的時(shí)間花銷

名稱	任務(wù)發(fā)布		任務(wù)接收		交易上傳
名稱	任務(wù)大小(kb)	處理時(shí)間(ms)	任務(wù)大小(kb)	處理時(shí)間(ms)	任務(wù)大小(kb)	處理時(shí)間(ms)
Task_50	1179.59	489.40	1289.69	4.47	4.7945	245.67
Task_100	2356.45	620.43	2416.15	7.89	9.7255	245.69
Task_150	3552.86	722.71	3932.77	13.79	14.0229	245.65
Task_200	4841.76	905.32	4825.98	11.63	21.3921	245.67
Task_250	5761.84	1219.45	5832.97	18.23	25.7526	478.90

下載: 導(dǎo)出CSV

參考文獻(xiàn)(15)

熊金波, 馬蓉, 牛犇, 等. 移動(dòng)群智感知中基于用戶聯(lián)盟匹配的隱私保護(hù)激勵(lì)機(jī)制[J]. 計(jì)算機(jī)研究與發(fā)展, 2018, 55(7): 1359–1370. doi: 10.7544/issn1000-1239.2018.20180080

XIONG Jinbo, MA Rong, NIU Ben, et al. Privacy protection incentive mechanism based on user-union matching in mobile crowdsensing[J]. Journal of Computer Research and Development, 2018, 55(7): 1359–1370. doi: 10.7544/issn1000-1239.2018.20180080

崔勇, 宋健, 繆蔥蔥, 等. 移動(dòng)云計(jì)算研究進(jìn)展與趨勢(shì)[J]. 計(jì)算機(jī)學(xué)報(bào), 2017, 40(2): 273–295. doi: 10.11897/SP.J.1016.2017.00273

CUI Yong, SONG Jian, Miao Congcong, et al. Mobile cloud computing research progress and trends[J]. Chinese Journal of Computers, 2017, 40(2): 273–295. doi: 10.11897/SP.J.1016.2017.00273

何云華, 李夢(mèng)茹, 李紅, 等. 群智感知應(yīng)用中基于區(qū)塊鏈的激勵(lì)機(jī)制[J]. 計(jì)算機(jī)研究與發(fā)展, 2019, 56(3): 544–554. doi: 10.7544/issn1000-1239.2019.20170670

HE Yunhua, LI Mengru, LI Hong, et al. A blockchain based incentive mechanism for crowdsensing applications[J]. Journal of Computer Research and Development, 2019, 56(3): 544–554. doi: 10.7544/issn1000-1239.2019.20170670

HUANG Kuanlun, KANHERE S S, and HU Wen. Preserving privacy in participatory sensing systems[J]. Computer Communications, 2010, 33(11): 1266–1280. doi: 10.1016/j.comcom.2009.08.012

DONG Kai, GU Tao, TAO Xianping, et al. Privacy protection in participatory sensing applications requiring fine-grained locations[C]. The 16th IEEE International Conference on Parallel and Distributed Systems, Shanghai, China, 2010. doi: 10.1109/ICPADS.2010.127.

CHRISTIN D, GUILLEMET J, REINHARDT A, et al. Privacy-preserving collaborative path hiding for participatory sensing applications[C]. The 8th IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Valencia, Spain, 2011: 341–350. doi: 10.1109/MASS.2011.41.

徐哲, 李卓, 陳昕. 面向移動(dòng)群智感知的多任務(wù)分發(fā)算法[J]. 計(jì)算機(jī)應(yīng)用, 2017, 37(1): 18–23, 47. doi: 10.11772/j.issn.1001-9081.2017.01.0018

XU Zhe, LI Zhuo, and CHEN Xin. Multi-task assignment algorithm for mobile crowdsensing[J]. Journal of Computer Applications, 2017, 37(1): 18–23, 47. doi: 10.11772/j.issn.1001-9081.2017.01.0018

CHRISTIN D, RO?KOPF C, HOLLICK M, et al. IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications[J]. Pervasive and Mobile Computing, 2013, 9(3): 353–371. doi: 10.1016/j.pmcj.2013.01.003

RESTUCCIA F and DAS S K. FIDES: A trust-based framework for secure user incentivization in participatory sensing[C]. IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks 2014, Sydney, Australia, 2014: 1–10.

CHANG S H, CHEN Y S, and CHENG S M. Detection of Sybil attacks in participatory sensing using cloud based trust management system[C]. 2013 International Symposium on Wireless and Pervasive Computing, Taipei, China, 2013: 1–6. doi: 10.1109/ISWPC.2013.6707448.

LI Ming, WENG Jian, YANG Anjia, et al. CrowdBC: A blockchain-based decentralized framework for crowdsourcing[J]. IEEE Transactions on Parallel and Distributed Systems, 2019, 30(6): 1251–1266. doi: 10.1109/TPDS.2018.2881735

LU Yuan, TANG Qiang, and WANG Guiling. ZebraLancer: Crowdsource knowledge atop open blockchain, privately and anonymously[J]. arXiv: 1803.01256v4, 2018.

嚴(yán)云洋, 瞿學(xué)新, 朱全銀, 等. 基于離群點(diǎn)檢測(cè)的分類結(jié)果置信度的度量方法[J]. 南京大學(xué)學(xué)報(bào): 自然科學(xué), 2019, 55(1): 102–109. doi: 10.13232/j.cnki.jnju.2019.01.010

YAN Yunyang, QU Xuexin, ZHU Quanyin, et al. Confidence measure method of classification results based on outlier detection[J]. Journal of Nanjing University:Natural Science, 2019, 55(1): 102–109. doi: 10.13232/j.cnki.jnju.2019.01.010

張俊松, 甘勇, 賀蕾. 群智感知環(huán)境下支持激勵(lì)機(jī)制實(shí)施的匿名身份認(rèn)證協(xié)議研究[J]. 小型微型計(jì)算機(jī)系統(tǒng), 2018, 39(7): 1522–1526. doi: 10.3969/j.issn.1000-1220.2018.07.027

ZHANG Junsong, GAN Yong, and HE Lei. Anonymous authentication protocol for supporting incentive mechanism in crowd sensing[J]. Journal of Chinese Computer Systems, 2018, 39(7): 1522–1526. doi: 10.3969/j.issn.1000-1220.2018.07.027

JUNG T, LI Xiangyang, and WAN Meng. Collusion-tolerable privacy-preserving sum and product calculation without secure channel[J]. IEEE Transactions on Dependable and Secure Computing, 2015, 12(1): 45–57. doi: 10.1109/TDSC.2014.2309134

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