本文選題：軌跡隱私保護(hù)　切入點(diǎn)：DTW距離函數(shù)　出處：《江西理工大學(xué)》2017年碩士論文

【摘要】：隨著移動(dòng)網(wǎng)絡(luò)和定位設(shè)備的飛速發(fā)展,各種移動(dòng)應(yīng)用中的數(shù)據(jù)成井噴式增長(zhǎng),致使大數(shù)據(jù)普遍存在。同時(shí),由于各種公司和研究機(jī)構(gòu)對(duì)數(shù)據(jù)的分析與挖掘。因此,只要存在數(shù)據(jù)的地方,就存在數(shù)據(jù)安全隱患。保證發(fā)布數(shù)據(jù)的軌跡隱私不泄露是本文的研究目的。使用經(jīng)典軌跡k-匿名算法對(duì)軌跡信息進(jìn)行發(fā)布的過程中,構(gòu)造軌跡等價(jià)類造成較大信息損失,導(dǎo)致數(shù)據(jù)可用性較低。同時(shí),經(jīng)典軌跡匿名算法使用歐幾里德函數(shù)度量軌跡距離時(shí),無法降低噪聲采樣點(diǎn)給距離度量帶來的較大誤差。為此,本文著重對(duì)降低軌跡等價(jià)類構(gòu)造信息損失和避免噪聲干擾的軌跡匿名算法進(jìn)行研究。本文的研究?jī)?nèi)容主要分為以下幾部分:1.經(jīng)典算法基于歐幾里德距離度量函數(shù)的軌跡相似性計(jì)算過程中,要求時(shí)間點(diǎn)一一對(duì)應(yīng),無法度量有局部時(shí)間偏移的軌跡間的相似性。在構(gòu)造軌跡等價(jià)類的過程中刪除較多位置信息,對(duì)軌跡數(shù)據(jù)的可用性影響較大。為此,本文提出了一種可以度量不等長(zhǎng)距離度量函數(shù)——DTW距離函數(shù),并結(jié)合一種新的軌跡匿名模型——(k,?,p)-匿名模型,構(gòu)造了DTW-TA軌跡匿名算法。在滿足軌跡隱私保護(hù)要求的前提下,有效地控制了信息損失。2.在傳統(tǒng)算法計(jì)算軌跡距離時(shí),由于噪聲采樣點(diǎn)的存在,會(huì)導(dǎo)致軌跡距離出現(xiàn)較大誤差,降低軌跡相似性,降低軌跡數(shù)據(jù)的精度。針對(duì)這一情況,本文結(jié)合LCSS距離函數(shù)和(k,?)-匿名模型提出了LCSS-TA軌跡匿名算法。該算法通過將兩個(gè)軌跡采樣點(diǎn)之間的距離映射成0或1,降低了噪聲采樣點(diǎn)導(dǎo)致的距離誤差,有效地提高軌跡信息的實(shí)用性。綜上所述,本文提出的基于DTW距離函數(shù)的DTW-TA軌跡匿名算法使發(fā)布軌跡數(shù)據(jù)的可用性得到有效提高,k-匿名改進(jìn)模型下的LCSS-TA算法較大地降低了噪聲點(diǎn)對(duì)軌跡距離度量的影響,提高了軌跡數(shù)據(jù)的精度。
[Abstract]:With the rapid development of mobile network and positioning equipment, the data in various mobile applications are blowout growth, which makes big data widely exist. At the same time, because of the analysis and mining of data by various companies and research institutions, As long as there are places where data exist, there are hidden dangers of data security. The purpose of this paper is to ensure that the privacy of the trajectory of the published data is not leaked. In the process of publishing the trace information, we use the classical trajectory k- anonymous algorithm to publish the trace information. The construction of locus equivalents causes a great loss of information, which results in low availability of data. Meanwhile, when the classical trajectory anonymous algorithm uses Euclidean function to measure the distance of trajectory, It is impossible to reduce the large error caused by noise sampling points to distance measurement. For this reason, In this paper, we focus on the research of trajectory anonymity algorithm, which can reduce the loss of information and avoid noise interference. The main contents of this paper are as follows: 1. The classical algorithm is based on Euclidean distance metric function. In the course of calculating the trajectory similarity of, It is impossible to measure the similarity of locus with local time offset because of the one-to-one correspondence of time points. In the process of constructing locus equivalence class, more position information is deleted, which has a great influence on the usability of locus data. In this paper, we present a new metric function, DTW distance function, which can be used to measure unequal distances, and combine it with a new locus anonymous model. In this paper, a DTW-TA path anonymous algorithm is constructed, which can effectively control the loss of information under the premise of satisfying the requirement of trajectory privacy protection. 2. When the traditional algorithm is used to calculate the trajectory distance, because of the presence of noise sampling points, In this paper, LCSS distance function and LCSS distance function are combined to reduce the accuracy of trajectory data. By mapping the distance between two locus sampling points to 0 or 1, the algorithm reduces the distance error caused by noise sampling points and effectively improves the practicability of trajectory information. An anonymous DTW-TA locus algorithm based on DTW distance function is proposed in this paper, which can effectively improve the availability of published trajectory data and reduce the influence of noise points on trajectory distance measurement. The accuracy of track data is improved.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP309

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 徐勇;李明;張慧;王有剛;黃淑芹;;基于K-匿名的軌跡隱私保護(hù)相關(guān)技術(shù)研究[J];價(jià)值工程;2015年35期

2 王超;楊靜;張健沛;;基于軌跡位置形狀相似性的隱私保護(hù)算法[J];通信學(xué)報(bào);2015年02期

3 王超;楊靜;張健沛;;基于軌跡特征及動(dòng)態(tài)鄰近性的軌跡匿名方法研究[J];自動(dòng)化學(xué)報(bào);2015年02期

4 黃石平;顧金媛;;一種基于(p~+,α)-敏感k-匿名的增強(qiáng)隱私保護(hù)模型[J];計(jì)算機(jī)應(yīng)用研究;2014年11期

5 張嘯劍;孟小峰;;面向數(shù)據(jù)發(fā)布和分析的差分隱私保護(hù)[J];計(jì)算機(jī)學(xué)報(bào);2014年04期

6 熊平;朱天清;王曉峰;;差分隱私保護(hù)及其應(yīng)用[J];計(jì)算機(jī)學(xué)報(bào);2014年01期

7 李建中;劉顯敏;;大數(shù)據(jù)的一個(gè)重要方面:數(shù)據(jù)可用性[J];計(jì)算機(jī)研究與發(fā)展;2013年06期

8 吳英杰;唐慶明;倪巍偉;孫志揮;廖尚斌;;基于聚類雜交的隱私保護(hù)軌跡數(shù)據(jù)發(fā)布算法[J];計(jì)算機(jī)研究與發(fā)展;2013年03期

9 楊靜;王波;;一種基于最小選擇度優(yōu)先的多敏感屬性個(gè)性化l-多樣性算法[J];計(jì)算機(jī)研究與發(fā)展;2012年12期

10 劉大有;陳慧靈;齊紅;楊博;;時(shí)空數(shù)據(jù)挖掘研究進(jìn)展[J];計(jì)算機(jī)研究與發(fā)展;2013年02期

，

本文編號(hào)：1698053