Leveraging Client Processing for Location Privacy in Mobile Local Search

University of Denver Digital Commons @ DU Electronic Theses and Dissertations Graduate Studies 1-1-2016 Leveraging Client Processing for Location Privacy in Mobile Local Search Wisam Mohamed Eltarjaman University of Denver Follow this and additional works at: https://digitalcommons.du.edu/etd Part of the Databases and Information Systems Commons, and the Information Security Commons Recommended Citation Eltarjaman, Wisam Mohamed, "Leveraging Client Processing for Location Privacy in Mobile Local Search" (2016). Electronic Theses and Dissertations. 1218. https://digitalcommons.du.edu/etd/1218 This Dissertation is brought to you for free and open access by the Graduate Studies at Digital Commons @ DU. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons @ DU. For more information, please contact [email protected],[email protected]. Leveraging Client Processing for Location Privacy in Mobile Local Search A Dissertation Presented to the Faculty of the Daniel Felix Ritchie School of Engineering and Computer Science University of Denver In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy by Wisam M. Eltarjaman November 2016 Advisor: Prof. Ramakrishna Thurimella Co Advisor: Dr. Rinku Dewri Author: Wisam M. Eltarjaman Title: Leveraging Client Processing for Location Privacy in Mobile Local Search Advisor: Prof. Ramakrishna Thurimella Co Advisor: Dr. Rinku Dewri Degree Date: November 2016 Abstract Usage of mobile services is growing rapidly. Most Internet-based services targeted for PC based browsers now have mobile counterparts. These mobile counterparts often are enhanced when they use user’s location as one of the inputs. Even some PC-based services such as point of interest Search, Mapping, Airline tickets, and software download mirrors now use user’s location in order to enhance their services. Location-based services are exactly these, that take the user’s location as an input and enhance the experience based on that. With increased use of these services comes the increased risk to location privacy. The location is considered an attribute that user’s hold as important to their privacy. Compromise of one’s location, in other words, loss of location privacy can have several detrimental effects on the user ranging from trivial annoyance to unreasonable persecution. More and more companies in the Internet economy rely exclusively on the huge data sets they collect about users. The more detailed and accurate the data a company has about its users, the more valuable the company is considered. No wonder that these companies are often the same companies that offer these services for free. This gives them an opportunity to collect more accurate location information. Research community in the location privacy protection area had to reciprocate by modeling an adversary that could be the service provider itself. To further drive this point, we show that a well-equipped service provider can infer user’s location even if the location information is not directly available by using other information he collects about the user. There is no dearth of proposals of several protocols and algorithms that protect location privacy. A lot of these earlier proposals require a trusted third party to play as an intermediary between the service provider and the user. These protocols use anonymization ii and/or obfuscation techniques to protect user’s identity and/or location. This requirement of trusted third parties comes with its own complications and risks and makes these proposals impractical in real life scenarios. Thus it is preferable that protocols do not require a trusted third party. We look at existing proposals in the area of private information retrieval. We present a brief survey of several proposals in the literature and implement two representative algorithms. We run experiments using different sizes of databases to ascertain their practicability and performance features. We show that private information retrieval based protocols still have long ways to go before they become practical enough for local search applications. We propose location privacy preserving mechanisms that take advantage of the processing power of modern mobile devices and provide configurable levels of location privacy. We propose these techniques both in the single query scenario and multiple query scenario. In single query scenario, the user issues a query to the server and obtains the answer. In the multiple query scenario, the user keeps sending queries as she moves about in the area of interest. We show that the multiple query scenario increases the accuracy of adversary’s determination of user’s location, and hence improvements are needed to cope with this situation. So, we propose an extension of the single query scenario that addresses this riskier multiple query scenario, still maintaining the practicability and acceptable performance when implemented on a modern mobile device. Later we propose a technique based on differential privacy that is inspired by differential privacy in statistical databases. All three mechanisms proposed by us are implemented in realistic hardware or simulators, run against simulated but real life data and their characteristics ascertained to show that they are practical and ready for adaptation. This dissertation study the privacy issues for location-based services in mobile environ- ment and proposes a set of new techniques that eliminate the need for a trusted third party by implementing efficient algorithms on modern mobile hardware. iii Acknowledgments My whole gratitude goes first to Allah Almighty. I thank my advisors Dr. Ramki Thurimella and Dr. Rinku Dewri, for their guidance, patience and more importantly their wholehearted support. No dissertation can come to fruition without the advisor’s guidance and mine is no different. But mine would not have happened without their support that was immensely needed and useful during these eventful several years of my research. I want to thank my committee members Dr. Matthew Rutherford and Dr. Nathan Sturtevant, whose constructive criticism made me think, and greatly improved the final product. My thanks go to fellow student Prasad Annadata, who did draft reviews and served as a sounding board for my ideas. My sincere thanks go to Mr. Andre Roudik, who often bent over backward to cleverly fulfill always urgent and big requests, always with a smile. He has become a true friend. Lastly, my thanks to Ms. Susan Bolton, often working in the background and making sure things run smoothly and often keep us in check, always available and took on whatever we threw at her. The study years for me at the University of Denver, have been eventful, to say the least. My family and I went through several events any of which would have easily broken my resolve to keep going. This would not have happened without His blessings. The blessings came in the form of my family that went through the roller coaster of the last few years with me, supporting me wholeheartedly, believing in me and more importantly motivating me to get this done. My deepest gratitude goes to my wife and kids. iv Contents 1 Introduction 1 1.1 Mobile Local Search ............................... 3 1.2 Impacts of Location Privacy Breaches ..................... 6 1.3 Location Privacy Attacks ............................ 8 1.3.1 Identity inference ............................. 8 1.3.2 Predicting user’s location of interest .................. 10 1.4 Contribution ................................... 11 1.5 Dissertation Outline ............................... 12 2 Location Privacy Mechanisms 14 2.1 Policy Based Protection ............................. 14 2.1.1 Access control policy ........................... 15 2.1.2 Privacy policy .............................. 16 2.2 Anonymization .................................. 17 2.2.1 k-anonymity ............................... 18 2.2.2 Major problems with anonymization .................. 22 2.3 Obfuscation .................................... 24 2.4 Spatio-temporal Cloaking ............................ 29 2.5 Cryptography Based Mechanisms ........................ 30 2.6 Differential Privacy ................................ 33 2.6.1 Intuition of differential privacy ..................... 33 2.6.2 Differential privacy for location data .................. 36 2.7 Motivation .................................... 37 2.8 Problem Statement ................................ 39 3 State of User’s Location Privacy 41 3.1 Collecting Driving Habits Data: Problem Definition ............. 42 3.2 Location Privacy Model ............................. 44 3.3 Driving Habits Data ............................... 45 3.3.1 Data collection .............................. 46 3.3.2 Pre-processing .............................. 46 3.4 Location Inference Method ........................... 47 3.4.1 Area map as a graph ........................... 48 3.4.2 Generating candidate paths ....................... 49 3.4.3 Candidate ranking ............................ 51 v 3.5 Empirical Observations ............................. 54 3.5.1 Illustrative example ........................... 55 3.5.2 Ranking performance .......................... 56 3.6 Summary ..................................... 58 4Architecture 59 4.1 Privacy in Local Search ............................. 59 4.2 Non-private Architecture ............................. 63 4.3 Request–Response Protocol ........................... 64 4.3.1 First round-trip .............................

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