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Recommending Privacy Settings for Internet-Of-Things Clemson University TigerPrints All Dissertations Dissertations December 2019 Recommending Privacy Settings for Internet-of-Things Yangyang He Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations Recommended Citation He, Yangyang, "Recommending Privacy Settings for Internet-of-Things" (2019). All Dissertations. 2528. https://tigerprints.clemson.edu/all_dissertations/2528 This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Recommending Privacy Settings for Internet-of-Things A Dissertation Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Computer Science by Yang He December 2019 Accepted by: Dr. Bart P. Knijnenburg, Committee Chair Dr. Larry F. Hodges Dr. Alexander Herzog Dr. Ilaria Torre Abstract Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting privacy preferences for numerous different IoT devices. Hence, there is a demand to solve the following, urgent research question: How can we help users simplify the task of managing privacy settings for IoT devices in a user-friendly manner so that they can make good privacy decisions? To solve this problem in the IoT domain, a more fundamental understanding of the logic behind IoT users' privacy decisions in different IoT contexts is needed. We, therefore, conducted a series of studies to contextualize the IoT users' decision-making characteristics and designed a set of privacy-setting interfaces to help them manage their privacy settings in various IoT contexts based on the deeper understanding of users' privacy decision behaviors. In this dissertation, we first present three studies on recommending privacy settings for different IoT environments, namely general/public IoT, household IoT, and fitness IoT, respectively. We developed and utilized a \data-driven" approach in these three studies|We first use statistical analysis and machine learning techniques on the collected user data to gain the underlying insights of IoT users' privacy decision behavior and then create a set of \smart" privacy defaults/profiles based on these insights. Finally, we design a set of interfaces to incorporate these privacy default/profiles. Users can apply these smart defaults/profiles by either a single click or by answering a few related questions. The biggest limitation of these three studies is that the proposed interfaces have not been tested, so we do not know what level of complexity (both in terms of the user interface and the in terms of the profiles) is most suitable. Thus, in the last study, we address this limitation by conducting a user study to evaluate the new interfaces of recommending privacy settings for household IoT users. The results show that our proposed user interfaces for setting household IoT privacy settings can improve users' satisfaction. Our research can benefit IoT users, manufacturers, ii Yang He Dissertation and researchers, privacy-setting interface designers and anyone who wants to adopt IoT devices by providing interfaces that put their most prominent concerns in the forefront and that make it easier to set settings that match their preferences. iii Dedication To my wife Mandy, my parents, and my friends in recognition of their undying support! iv Acknowledgments First and foremost, I would like to express immense gratitude to my advisor, Dr. Bart Knijnenburg, for his support, patience, and understanding during the past four years. Second, I would like to thank my committee members, Dr. Larry Hodges, Dr. Alexander Herzog, and Dr. Ilaria Torre, for their help and suggestions they provided on my research. Third, I want to thank Paritosh, Reza, and Byron for their friendship and inspiration. Last but not least, I want to thank my parents and my wife Mandy who have supported me throughout this long journey. Financial support was provided by Clemson University, NSF grants 1640664 and CNS- 1126344, and a gift from Samsung Research America. v Outline Abstract ............................................. ii Dedication............................................ iv Acknowledgments ....................................... v List of Tables ..........................................viii List of Figures.......................................... ix 1 Introduction......................................... 1 2 IoT technology and IoT Acceptance........................... 5 2.1 IoT Technology....................................... 6 2.2 Model the Acceptance of IoT............................... 7 2.3 Summary .......................................... 10 3 Privacy setting technologies in IoT ...........................11 3.1 Privacy Preference..................................... 11 3.2 Privacy in IoT ....................................... 12 3.3 Existing Privacy Setting Models ............................. 13 3.4 Privacy-Setting Interfaces................................. 15 3.5 Privacy Prediction ..................................... 15 3.6 Summary .......................................... 17 4 Recommending Privacy Settings for General/Public IoT..............18 4.1 Introduction......................................... 18 4.2 Dataset and design..................................... 20 4.3 Statistical Analysis..................................... 22 4.4 Predicting users' behaviors (original work)........................ 23 4.5 Privacy shortcuts (original work)............................. 33 4.6 Discussion and Limitations ................................ 35 4.7 Summary .......................................... 36 5 Recommending Privacy Settings for Household IoT.................37 5.1 Introduction......................................... 37 5.2 Experiment Setup ..................................... 38 5.3 Statistical Analysis..................................... 46 5.4 Privacy-Setting Prototype Design............................. 47 5.5 Predicting users' behaviors (original work)........................ 49 5.6 Privacy-Setting Prototype Design Using Machine Learning Results (original work) . 66 5.7 Limitations ......................................... 71 vi Yang He Dissertation 5.8 Summary .......................................... 72 6 Recommending Privacy Settings for Fitness IoT...................73 6.1 Introduction......................................... 73 6.2 Data Model......................................... 74 6.3 Dataset ........................................... 77 6.4 Predicting users' Preference (partial original work)................... 78 6.5 Profile Prediction (partial original work)......................... 82 6.6 Privacy-setting Recommendations (partial original work)................ 89 6.7 Validation.......................................... 91 6.8 Summary .......................................... 92 7 Evaluate the Household IoT Privacy-setting Profiles and User Interfaces . .100 7.1 Introduction......................................... 100 7.2 Study Design........................................ 101 7.3 Experimental setup..................................... 105 7.4 Results............................................ 106 7.5 Discussion.......................................... 113 8 Conclusion..........................................116 Bibliography...........................................117 Appendices ...........................................128 vii List of Tables 4.1 Parameters used in the experiment............................ 21 4.2 Comparison of clustering approaches........................... 25 4.3 Confusion matrix for the overall prediction ....................... 25 4.4 Drill down of the Overall Prediction tree for `who' = `Own device'.......... 27 5.1 Parameters used to construct the information-sharing scenarios............. 42 5.2 Comparison of clustering approaches (highest parsimony and highest accuracy) . 50 5.3 Confusion matrix for the One Rule prediction...................... 51 5.4 Confusion matrix for the overall prediction ....................... 53 7.1 Factor Items in Trimmed CFA Model .......................... 108 A1 Table of Accuracies. .................................... 130 viii List of Figures 2.1 The factors that affecting users' adoptions of IoT found in our study......... 9 2.2 Trust Chain......................................... 10 4.1 From Left, Screen 1 shows three default settings, Screen 2,3 and 4 shows layered interface........................................... 24 4.2 The Overall Prediction decision tree. Further drill down for `who' = `Own device' is provided in Table 4.4.................................... 26 4.3 Attitude-based clustering: 2-cluster tree. ........................ 28 4.4 Attitude-based clustering: 3-cluster tree......................... 28 4.5 The Flow Chart for Fit-based Clustering......................... 30 4.6 Fit-based clustering: 3-cluster tree. Further drill down is hidden for space reasons. 31 4.7 Accuracy of our clustering approaches .......................... 32 4.8 Two types of profile choice interfaces........................... 34 5.1 Example of one of the thirteen scenarios presented to the participants. 41 5.2 Attention check questions asked to participants..................... 43 5.3 Transcript of video shown to participants if they failed
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