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研究生:宋建凱
研究生(外文):Jian-Kai Sung
論文名稱:適地性服務隱私保護之敏感型樣集合隱匿技術
論文名稱(外文):On Cloaking Sensitive Pattern Set for LBS Privacy Preserving
指導教授:蔡曉萍蔡曉萍引用關係
口試委員:胡誌麟鄧維光吳國光
口試日期:2016-07-27
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:42
中文關鍵詞:LBS隱私保護移動型樣型樣組合偽裝區域
外文關鍵詞:LBS Privacy PreservingMovement PatternCombination of PatternsCloak Region
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今日LBS的應用在我們的生活中到處可見,如導航、運動軟體等,但LBS應用也被認為有很大的隱私疑慮,因為當要使用LBS查詢鄰近的資訊時,使用者必須將自己目前的地理位置傳送給LBS提供者,目前已有很多位置或軌跡的隱私保護技術被提出,但大多數的技術都沒有考慮到使用者的長期移動規律性,一般大眾每天的移動行為都具有強烈的規律性,移動規律性又稱為移動型樣,型樣因為是使用者習慣行為,所以容易被觀察也經常被用於側寫使用者,當使用者的位置資訊被大量的收集後,攻擊者如LBS提供者,可以從使用者的位置或軌跡資訊中探勘出使用者的移動型樣,進而使用型樣把使用者從匿名的使用者編號中辨識出來。
為了要解決上述問題,除了保護保護敏感的位置之外,我們更加考慮保護使用者的型樣,我們定義Secure Pattern-safe Cloak (SPSC) region去隱匿敏感型樣並且套用K-anonymity模型,但我們發現到兩個不敏感的型樣組合或型樣和偽裝區域的組合也可用於辨識匿名的使用者,因此我們彙整出型樣隱私保護(p3)的問題,去確保大小小於或等於M的型樣組合都必須符合K-anonymity的模型,為了去處理p3之問題,我們提出Apriori-like Privacy Preserving (APP)演算法,原始Apriori是在每遞迴中找出高頻資料集,APP演算法不同的是在每次遞迴中找出敏感的型樣組合,一旦找出敏感的型樣組合,APP演算法為每個敏感組合計算出可使用的SPSC region,並選擇其中面積最小的做為最後的SPSC region。為了要去計算敏感的型樣組合和偽裝區域,我們提出了一個隱私感知之LBS資料擷取系統,此系統可以在使用者做LBS查詢時得到適當的隱私保護。最後,我們使用真實的軌跡資料去測試我們的演算法的效能與影響,在初期的實驗中顯示p3的問題非常嚴重,我們的演算法可以快速的找出敏感的型樣組合並且提供適當的保護,實驗結果也顯示,在絕大部分的情況下以一個實際的地理位置和使用偽裝區域做LBS查詢時,回傳資料的有效性都是可以接受的。

Nowadays, LBS applications have been very prevalent in our daily life, such as navigation. Nevertheless, LBS are considered of server privacy issues because users have to send out personal locations to LBS providers in order to query location dependent information. Many location-based or trajectory-based privacy preserving techniques are proposed. However, few of them take user’s long-term movement regularities into consideration. Note that there are a lot of movement regularities in our daily life. The regular and high frequent patterns are more easily observed and can be used to profile users. While a large amount of user’s location data is collected, the adversaries like LBS providers may mine movement patterns in the data and then use them to re-identify the anonymous users.
In the view of this, except for protecting sensitive location, we further consider to protect user’s sensitive patterns in LBS privacy preserving. Conforming to K-anonymity model, we define the Secure Pattern-safe Cloak (SPSC) region for a sensitive pattern. Moreover, we observe that combination of insensitive patterns and even cloak regions can also be used to identify user. Thus, we formulate the pattern-based privacy protection (p3) problem to ensure individual user’s pattern sets with size less than and equal to M must be K-anonymous. To tackle the p3 problem, we propose the Apriori-like privacy preserving (APP) algorithm that exams combinations of patterns for sensitive one in a manner like the Apriori algorithm for large itemset and then deal with sensitive pattern sets in a greedy manner. Specifically, it first figures out sensitive pattern sets and for each sensitive pattern set, it computes the cost of cloaking a pattern or a subset of the pattern set of the pattern set and finally chooses the one with lowest cost. Therefore, with the information of sensitive patterns and their SPSC regions, we propose the privacy-aware LBS system that can provide pattern-based privacy protection while using LBS. Finally, we conduct several experiments with a real dataset to study the performance and effects of our design. The preliminary results show that the p3 problem is server and the proposed APP algorithm can help figure out sensitive pattern with less cost. In addition, the utility of the query results in the cloak region can be acceptable in many query situations.


中文摘要 iii
Abstract iv
目錄 v
表目次 vi
圖目次 vii
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究問題 7
1.4 研究方法 7
1.5 論文架構 7
第2章 相關研究 8
2.1 隱私保護模型 8
2.1.1 K-anonymity 8
2.1.2 L-diversity 9
2.2 APRIORI ALGORITHM 9
2.3 LBS的分類及隱私疑慮 11
2.4 LBS隱私保護機制分類 12
2.5 SPATIAL LOCATION CLOAKING TECHNIQUE 12
2.6 隱私保護伺服器的分類 14
2.7 空間資料索引技術 15
第3章 問題分析與系統架構 17
3.1 型樣隱私保護之問題 17
3.2 隱私保護系統架構 18
第4章 Apriori-like Privacy Preserving Technique for LBS 21
4.1 SECURE PATTERN-SAFE CLOAKING REGION定義 21
4.2 APRIORI-LIKE PRIVACY PRESERVING (APP) ALGORITHM 23
第5章 實驗結果 29
5.1 實驗環境與資料 29
5.2 實驗結果與分析 34
5.2.1 型樣組合大小對於SPSC region總面積之影響 34
5.2.2 資料密度對於搜尋結果準確度影響 35
5.2.3 L-diversity 對於執行時間的影響 36
第6章 結論 38
參考文獻 39

[1] Chi-Yin Chow, and Mohamed F. Mokbel, “Trajectory Privacy in Location-based Services and Data Publication,” Proceedings of ACM SIGKDD Explorations Newsletter, Pages 19-29, 2011
[2] B. Bamba, L. Liu, P. Pesti, and T. ang., “Supporting anonymous location queries in mobile environments with PrivacyGrid,” Proceedings of the 17th international conference on World Wide Web, Pages 237-246, 2008
[3] R. Cheng, Y. Zhang, E. Bertino, and S. Prabhakar., “Preserving user location privacy in mobile data management infrastructures,” Proceedings of International Privacy Enhancing Technologies Symposium, Pages 393-412, 2006
[4] C.-Y. Chow, M. Mokbel, and T. He., “A privacypreserving location monitoring system for wireless sensor networks,” Proceeding of IEEE Transactions on Mobile Computing, Pages 94–107, 2011
[5] Marco Gruteser, and Dirk Grunwald, “Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking,” Proceedings of the 1st International Conference on Mobile systems, applications and services Pages 31-42
[6] Chi-Yin Chow, and Mohamed F. Mokbel, “Trajectory Privacy in Location-based Services and Data Publication,” Proceedings of ACM SIGKDD Explorations Newsletter, Pages 19-29, 2011
[7] Alastair R. Beresford, and Frank Stajano, “Mix Zones: User Privacy in Location-aware Services,” Proceedings of IEEE Workshop on Pervasive Computing and Communication Security, 2004
[8] R. Agrawal, and R. Srikant, “Mining Sequential Patterns,” Proceedings of the Eleventh International Conference on Data Engineering, Pages 3-14, 1995
[9] Muawya Habib, Sarnoub Eldaw, Mark Levene, and George Roussos, “Collective Suffix Tree-based Models for Location Prediction,” Proceedings of the ACM conference on Pervasive and ubiquitous computing adjunct publication, Pages 441-450, 2013
[10] Kari Laasonen, “Clustering and Prediction of Mobile User Routes from Cellular Data,” Proceedings of Knowledge Discovery in Databases, Pages 569-576, 2005
[11] F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, “Trajectory Pattern Mining,” Proceedings of 13th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, Pages 330-339, 2007
[12] V. S. Tseng, and K. W. Lin, “Energy Efficient Strategies for Object Tracking in Sensor Networks,” Proceedings of Journal Systems and Software, Pages 1678–1698, 2007
[13] M. Morzy, “Mining Frequent Trajectories of Moving Objects for Location Prediction,” Proceedings of 5th Int’l Conf. on Machine Learning and Data Mining in Pattern Recognition, Pages 667-680, 2007
[14] Ruggero G. Pensa, Anna Monreale, Fabio Pinelli, and Dino Pedreschi, “Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining,” Proceedings of the 1st International Workshop on Privacy in Location-Based Applications, 2008
[15] M. Morzy, “Mining Frequent Trajectories of Moving Objects for Location Prediction,” Proceedings of 5th Int’l Conf. on Machine Learning and Data Mining in Pattern Recognition, Pages 667-680, 2007
[16] T. Xu and Y. Cai, “Exploring Historical Location Data for Anonymity Preservation in Location-Based Services,” Proceeding of the 27th Conference on Computer Communications, 2008
[17] A. Beresford and F. Stajano, “Location Privacy in Pervasive Computing,” Proceeding of IEEE Pervasive Computing, Pages 46-55, 2003
[18] Ashwin Machanavajjhala, Johannes Gehrke, and Daniel Kifer, “ℓ-Diversity: Privacy Beyond k-Anonymity,” Proceeding of Journal ACM Transactions on Knowledge Discovery from Data (TKDD), Article No. 3, 2007
[19] L. Sweeney, “k-Anonymity: a model for protecting privacy,” Proceeding of International Journal on Uncertainty, Fuzziness and Knowledge-based Systems,
10 (5):557–570, 2002
[20] M. Gruteser;D., and Grunwald, “A methodological assessment of location privacy risks in wireless hotspot networks,” Proceedings of 1st International Conference, Boppard, Germany, March 12-14, 2003
[21] M. F. Mokbel, C. Chow, and W. G. Aref, “The new casper: Query processing for location services without compromising privacy,” Proceedings of the 32nd International Conference on Very large data bases, Pages 763-774, 2006
[22] Kalnis, P., Ghinita, G., Mouratidis, K., and Papadias, D. , “Preventing location-based identity inference in anonymous spatial queries,” Proceeding of Knowledge and Data Engineering, IEEE Transactions, Pages 1719–1733, 2007
[23] P. Kalnis, G. Ghinita; K.Mouratidis, and D. Papadias , “Preserving Location-based Identity Inference in Anonymous Spatial Queries,” Proceeding of Knowledge and Data Engineering, Pages 1719-1733, 2007
[24] B. Gedik, and L. Liu, “Location Privacy in Mobile Systems: A Personalized Anonymization Model,” Proceedings of 25th IEEE International Conference on Distributed Computing Systems, Pages 620-629, 2005
[25] Chow, C., Mokbel, M., and Liu, X., “A peer-to-peer spatial cloaking algorithm for anonymous ocation-based service,” Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, Pages 171-178, 2006
[26] G. Ghinita, P. Kalnis, and S. Skiadopoulos , “PRIVE: Anonymous Location-Based Queries in Distributed Mobile Systems,” Proceedings of the 16th international conference on World Wide Web, Pages 371-380, 2007
[27] Ghinita, G., Kalnis, P., Skiadopoulos, “MobiHide : A Mobilea Peer-to-Peer System for Anonymous Location-Based Queries,” Proceeding of International Symposium on Spatial and Temporal Databases, Pages 221-238, 2007
[28] Gedik, B. and Liu, L., “Location Privacy in Mobile Systems:
A Personalized Anonymization Model,” Proceeding of 25th IEEE International Conference on Distributed Computing Systems, Pages 620 – 629, 2005
[29] M. Morzy, “Mining Frequent Trajectories of Moving Objects for Location Prediction,” Proceedings of 5th Int’l Conf. on Machine Learning and Data Mining in Pattern Recognition, Pages 667-680, 2007
[30] T. Xu and Y. Cai, “Exploring Historical Location Data for Anonymity Preservation in Location-Based Services,” Proceeding of the 27th Conference on Computer Communications, 2008
[31] A. Beresford and F. Stajano, “Location Privacy in Pervasive Computing,” Proceeding of IEEE Pervasive Computing, Pages 46-55, 2003
[32] J. Meyerowitz and R. R. Choudhury, “Hiding Stars with Fireworks: Location Privacy Through Camouflage,” Proceedings of the 15th annual international conference on Mobile computing and networking, Pages 345-356, 2009
[33] J. Manweiler, R. Scudellari, and L. P. Cox, “Smile: Encounter-Based Trust for Mobile Social Services,” Proceedings of the 16th ACM conference on Computer and communications security, Pages 246-255, 2009
[34] H. Kido, Y. Yanagisawa, and T. Satoh, “An Anonymous Communication Technique Using Dummies for Location-based Services,” Proceeding of Int’l. Conf. Pervasive Services, July 2005
[35] A. Guttman, “R-Tree: A Dynamic Index Structure for Spatial Searching,” Proceedings of ACM SIGMODE, Pages 47-57, 1984
[36] Gabriel Ghinita, Panos Kalnis, Ali Khoshgozaran, Cyrus Shahabi, and Kian-Lee Tan, “Private Queries in Location Based Services: Anonymizers are not Necessary,” Proceedings of the ACM SIGMOD international conference on Management of data, Pages 121-132, 2008
[37] Ruggero G. Pensa, Anna Monreale, Fabio Pinelli, and Dino Pedreschi, “Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining,” Proceedings of the 1st International Workshop on Privacy in Location-Based Applications, 2008
[38] Hyeong-Il Kim, Yong-Ki Kim, and Jae-Woo Chang1, “A Grid-based Cloaking Area Creation Scheme for Continuous LBS Queries in Distributed Systems,” Proceeding of Journal of Convergence, vol. 4, no. 1, 2013
[39] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan., “Private queries in location based services: Anonymizers are not necessary,” Proceedings of the 2008 ACM SIGMOD international conference on Management of data, Pages 121-132, 2008
[40] J. I. Hong and J. A. Landay., “An architecture for privacy-sensitive ubiquitous computing,” Proceedings of the 2nd international conference on Mobile systems, applications, and services, Pages 177-189, 2004
[41]Kalnis, P., Ghinita, G., Mouratidis, K., and Papadias, D. , “Preventing location-based identity inference in anonymous spatial queries,” Proceeding of Knowledge and Data Engineering, IEEE Transactions, Pages 1719–1733, 2007
[42]Y. Wang, D. Xu, et al., L2P2: Location-aware Location Privacy Protection for Location-based Services, in INFOCOM’12: Proceeding of the IEEE International Conference on Computer Communications, pp. 1996–2004, 2012


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