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
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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
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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
A Mobile Crowdsensing Data Security Delivery Model Based on Tangle Network
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College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
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School of Computer Science and Technology, Harbin University of Scienceand Technology, Harbin 150080, China
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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。
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關(guān)鍵詞:
- 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.
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Key words:
- Mobile CrowdSensing (MCS) /
- Tangle network /
- Perceived data /
- Secure delivery
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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)$\pi (x) = ({x_{n1} },{x_{n1 + 1} },···,{x_{n1 + n2 - 1} })$。下載: 導(dǎo)出CSV
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 下載: 導(dǎo)出CSV
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 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
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