提供附近用戶新的社交環境是鄰近為基礎的行動社交網路一個重要的服務。鄰近為基礎的行動社交網路讓使用者透過自己的興趣去尋找或認識附近與自己興趣最相符的人。通常都是由手機應用程式的方式來實現，使用者下載手機應用程式填寫使興趣透過藍芽或Wi-Fi 或是後端伺服器的方式去尋找。其中一種方式是使用者(發起者) 將自己的興趣廣播給周圍的使用者，周圍的使用者比對興趣相符的個數和程度傳回給發起者，最後發起者找到與自己興趣相符的朋友。目前鄰近為基礎的行動社交網路存在四個問題，包含激勵問題、隱私問題、信任度問題和配對問題。在本篇論文中，我們要利用信譽系統來解決信任度問題。不過隱私保護和信譽系統的要求是有衝突的。因為在隱私保護上通常是要達到移除相同使用者之間的連結，可是信譽系統是需要長期觀察使用者的行為，因此相同使用者之間的連結在信譽系統是需要的。我們提供一個系統來解決衝突問題以降低使用者之間的連結被惡意使用者知道。最後我們針對目前常見的攻擊進行安全分析，利用ns2 模擬平台來驗證信譽系統是否精準和能否找出惡意使用者。

Proximity-based Mobile Social Networks (PMSNs) is a new type of social network
provides social interaction atmosphere using mobile devices such as smart phone to proximate mobile users nearby. In such application, a user applied wireless communication technology (Bluetooth or Wi-Fi) to make new social interactions with nearby friends. However, PMSNs has trustworthiness issue because PMSNs is an openness system that allows any user to upload information, therefore, a user could
receive erroneous information. In addition, a user can generate uncertainty information
or doubt the information of other users. Thus, PMSNs needs a trustworthy
algorithm to evaluate user’s authorization with protecting his/her privacy issue. In
this thesis we provide a trust model to solve trustworthiness issue by adopting reputation
system. However, in general, privacy and reputation systems are conflict. For
example, a good reputation system requires observing its user in a long–term period
and we need to know social links between users. Nevertheless, for privacy issue, we
have to remove links between users that can achieve clacking identity and sensitive
information. We proposed a protocol that prevents malicious users to know links
between users and tradeoffs between privacy and reputation system. We utilize this
protocol to statistics user’s trustworthiness score and find out malicious users. If
user’s trustworthiness score is lower than defined threshold, this user will be recognized
as a malicious user. Finally, we conduct simulation using our protocol to
confirm accuracy of user’s reputation score and find out malicious users.

[1] S. Ioannidis, A. Chaintreau, and L. Massoulie, “Optimal and scalable distribution
of content updates over a mobile social network,” in Proc. IEEE INFOCOM
2009, pp. 1422–1430, Apr. 2009.
[2] R. Lu, X. Lin, and X. Shen, “Spring: a social-based privacy-preserving packet
forwarding protocol for vehicular delay tolerant networks,” in Proc. IEEE INFOCOM
2010, pp. 1–9, Mar. 2010.
[3] D. Niyato, P. Wang, W. Saad, and A. Hjorungnes, “Controlled coalitional games
for cooperative mobile social networks,” IEEE Trans. Veh. Technol., vol. 60,
pp. 1812–1824, May 2011.
[4] M. Brereton, P. Roe, M. Foth, J. M. Bunker, and L. Buys, “Designing participation
in agile ridesharing with mobile social software,” in OZCHI, pp. 257–260,
Nov. 2009.
[5] E. Bulut and B. K. Szymanski, “Exploiting friendship relations for efficient
routing in delay tolerant mobile social networks,” in IEEE Trans. Parallel Distrib.
Syst., vol. 23, pp. 1–5.
[6] D. Yang, G. Xue, X. Fang, and J. Tang, “Crowdsourcing to smartphones: incentive
mechanism design for mobile phone sensing,” in MobiCom 2012, pp. 173–
184, 2012.
[7] N. Vastardis and K. Yang, “Mobile social networks: Architectures, social properties,
and key research challenges,” IEEE Communications Surveys & Tutorials,
vol. 15, no. 3, pp. 1355–1371, 2013.
[8] R. Gross and A. Acquisti, “Information revelation and privacy in online social
networks,” in WPES, pp. 71–80, 2005.
[9] G. Chen and F. Rahman, “Analyzing privacy designs of mobile social networking
applications,” in IEEE/IFIP Int. Conf. EUC 2008., vol. 2, pp. 83–88, 2008.
[10] I. Krontiris, F. C. Freiling, and T. Dimitriou, “Location privacy in urban sensing
networks: research challenges and directions,” vol. 17, pp. 30–35, Oct. 2010.
[11] K. P. N. Puttaswamy and B. Y. Zhao, “Preserving privacy in location-based
mobile social applications,” in Proc. HotMobile 2010, vol. 8, pp. 1–6, 2010.
[12] K. L. Huang, S. S. Kanhere, and W. Hu, “Are you contributing trustworthy
data? the case for a reputation system in participatory sensing,” in Proc.
MSWIM 2010, pp. 14–22, 2010.
[13] W. Dong, V. Dave, L. Qiu, and Y. Zhang, “Secure friend discovery in mobile
social networks,” in Proc. IEEE INFOCOM 2011, pp. 1647–1655, Apr. 2011.
[14] A.-K. Pietilainen, E. Oliver, J. LeBrun, G. Varghese, and C. Diot, “Mobiclique:
middleware for mobile social networking,” in Conf. SIGCOMM 2009, pp. 49–54,
Aug. 2009.
[15] S. Rane, W. Sun, and A. Vetro, “Privacy-preserving approximation of l1 distance
for multimedia applications,” in IEEE ICME 2010, pp. 492–497, July
2010.
[16] X. Liang, X. Li, K. Zhang, R. Lu, X. Lin, and X. Shen, “Fully anonymous
profile matching in mobile social networks,” vol. 31, pp. 641–655, Sept. 2013.
[17] M. Li, N. Cao, S. Yu, and W. Lou, “Findu: Privacy-preserving personal profile
matching in mobile social networks,” in Proc. IEEE INFOCOM 2011, pp. 2435–
2443, Apr. 2011.
[18] R. Zhang, Y. Zhang, J. Sun, and G. Yan, “Fine-grained private matching for
proximity-based mobile social networking,” in Proc. IEEE INFOCOM 2012,
pp. 1969–1977, Mar. 2012.
[19] R. Zhang, J. Zhang, Y. Zhang, J. Sun, and G. Yan, “Privacy-preserving profile
matching for proximity-based mobile social networking,” vol. 31, pp. 656–668,
Sept. 2013.
[20] A. Beach, M. Gartrell, and R. Han, “Solutions to security and privacy issues
in mobile social networking,” in Int. Conf. CSE 2009., vol. 4, pp. 1036–1042,
Aug. 2009.
[21] S. Gao, J. Ma, and W. Shi, “TrPF: A trajectory privacy-preserving framework
for participatory sensing,” IEEE Trans. Inf. Forensics Security, vol. 8, pp. 874–
887, June 2013.
[22] P. Kalnis, G. Ghinita, K. Mouratidis, and D. Papadias, “Preventing locationbased
identity inference in anonymous spatial queries,” IEEE Trans. Knowledge
Data Eng., vol. 19, no. 12, pp. 1719–1733, 2007.
[23] B. Gedik and L. Liu, “Protecting location privacy with personalized kanonymity:
Architecture and algorithms,” IEEE Trans. Mobile Comput., vol. 7,
no. 1, pp. 1–18, 2008.
[24] M. Shin, C. Cornelius, D. Peebles, A. Kapadia, D. Kotz, and N. Triandopoulos,
“Anonysense: A system for anonymous opportunistic sensing,” Pervasive and
Mobile Comput., vol. 7, no. 1, pp. 16–30, 2011.
[25] E. D. Cristofaro and C. Soriente, “PEPSI:privacy-enhanced participatory sensing
infrastructure,” in Proc. ACM WiSec, pp. 23–28, 2011.
[26] R. Paul, K. Ko, Z. Richard, and F. Eric, “Reputation systems,” CACM, vol. 43,
pp. 45–48, Dec. 2000.
[27] Y. mei Liu, S. bao Yang, L. tao Guo, W. ming Chen, and L. min Guo, “A
distributed trust-based reputation model in P2P system,” in Eighth ACIS Int.
Conf. SNPD 2007, vol. 1, pp. 294–299, Aug. 2007.
[28] M. Gupta, P. Judge, and M. Ammar, “A reputation system for peer-to-peer
networks,” in NOSSDAV, pp. 144–152, 2003.
[29] S. Buchegger and J.-Y. L. Boudec, “A robust reputation system for p2p
and mobile ad hoc networks P2P and mobile ad-hoc networks,” http://citeseer.
ist.psu.edu/, 2004.
[30] Y. Rebahi, V. E. Mujica-V, and D. Sisalem, “A reputation-based trust mechanism
for ad hoc networks,” in IEEE ISCC 2005, pp. 37–42, June 2005.
[31] M. Kinateder and S. Pearson, “A privacy-enhanced peer-to-peer reputation
system,” in E-Commerce and Web Technologies, pp. 206–215, 2003.
[32] Y. Wei and Y. He, “A pseudonym changing-based anonymity protocol for P2P
reputation systems,” in Proc. IEEE ETCS 2009, pp. 975–980, Mar. 2009.
[33] K. L. Huang, S. S. Kanhere, and W. Hu, “A privacy-preserving reputation
system for participatory sensing,” in Proc. IEEE LCN 2012, pp. 10–18, Oct.
2012.
[34] D. Christin, C. Roskopf, M. Hollick, L. Martucci, and S. Kanhere,
“IncogniSense: An anonymity-preserving reputation framework for participatory
sensing applications,” in Proc. IEEE PerCom 2012, pp. 135–143, Mar.
2012.
[35] E. Anceaume, G. Guette, P. L. Mazenc, T. Sirvent, and V. V. T. Tong, “Extending
signatures of reputation,” in Proc. IEEE ICC, pp. 1951–1956, June
2013.
[36] A. Ghose, P. G. Ipeirotis, and A. Sundararajan, “Opinion mining using econometrics:
A case study on reputation systems,” in ACL, vol. 45, p. 416, 2007.
[37] P. Resnick and R. Zeckhauser, “Trust among strangers in internet transactions:
Empirical analysis of ebay’s reputation system,” Advances in applied microeconomics,
vol. 11, pp. 127–157, 2002.
[38] R.-J. Hwang, F.-F. Su, Y.-S. Yeh, and C.-Y. Chen, “An efficient decryption
method for rsa cryptosystem,” in in IEEE Int. Conf. AINA 2005., vol. 1,
pp. 585–590, Mar. 2005.
[39] T. J. Fagan, “Letter: nomogram for bayes theorem,” The New England journal
of medicine, vol. 293, no. 5, pp. 257–257, 1975.
[40] S. Buchegger and J.-Y. L. Boudec, “A robust reputation system for mobile
ad-hoc networks,” EPFL IC Technical Report IC, 2003.
41] A. Hinneburg and D. A. Keim, “An efficient approach to clustering in large
multimedia databases with noise,” in KDD, vol. 98, pp. 58–65, 1998.
[42] A. H. A. Rahman and Z. A. Zukarnain, “Performance comparison of aodv, dsdv
and i-dsdv routing protocols in mobile ad hoc networks,” ESJ, vol. 31, no. 4,
pp. 566–576, 2009.
[43] N. Aschenbruck, R. Ernst, E. Gerhards-Padilla, and M. Schwamborn, “Bonnmotion:
a mobility scenario generation and analysis tool,” in ICST, p. 51,
2010.
[44] J. Yoon, M. Liu, and B. Noble, “Random waypoint considered harmful,” in
Proc. IEEE INFOCOM 2003, vol. 2, pp. 1312–1321, Mar. 2003.