Author(s):

  • Pei-Yao Hung
  • Mark S. Ackerman

Abstract:

With the advent of pervasive sensing devices, data captured about one’s everyday life (e.g., heart rate, sleep quality, emotion, or social activity) offers enormous possibilities for promoting in-home health care for severe chronic care, such as can be found in Spinal Cord Injury or Disorders or the like. Sharing these Everyday Data for Care (EDC) allows care team personnel (e.g., caregivers and clinicians) to assist with health monitoring and decision-making, but will also create tension and concerns (e.g., privacy) for people with health conditions due to the detailed nature of the data. Resolving these tensions and concerns is critical for the adoption and use of a pervasive healthcare environment.

We examine data sharing of EDC to determine how we can better manage the tradeoffs between privacy on one hand and the pro-active sharing of data that one needs for better care. In this paper, we target one critical aspect of using EDC, the problem of sharing an overwhelming number of sensor outputs with numerous care team recipients. We report the results of a scenario-based study that examined ways to reduce the burden of setting policies or rules to manage both the pro-active data sharing and the privacy aspects of care with EDC. In summary, we found that our participants were able to use self-generated groupings of EDC data, and more importantly, largely kept those groupings when creating to share data with potential recipients and when dealing with changes in their health trajectory. These findings offer hope that we can reduce the burden of authoring and maintaining data sharing and privacy policies through semi-automatic mechanisms, where the system suggests policies that are consistent with the users’ preferences – especially as health changes.

Documentation:

https://doi.org/10.1007/978-3-030-99194-4_18

References:
  1. Ackerman, M.S., Büyüktür, A.G., Hung, P.-Y., Meade, M.A., Newman, M.W.: Socio-technical design for the care of people with spinal cord injuries. In: Designing Healthcare That Works, pp. 1–18. Elsevier (2018) Google Scholar 
  2. Ackerman, M.S., et al.: Simplifying user-controlled privacy policies. IEEE Pervasive Comput. 8, 28–32 (2009)CrossRef  Google Scholar 
  3. Almuhimedi, H., et al.: Your location has been shared 5,398 times!: a field study on mobile app privacy nudging. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 787–796. ACM, New York (2015). https://doi.org/10.1145/2702123.2702210. http://doi.acm.org/10.1145/2702123.2702210
  4. Amir, O., Grosz, B.J., Gajos, K.Z., Swenson, S.M., Sanders, L.M.: From care plans to care coordination: opportunities for computer support of teamwork in complex healthcare. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 1419–1428. ACM, New York (2015). https://doi.org/10.1145/2702123.2702320. http://doi.acm.org/10.1145/2702123.2702320
  5. Anderson, G.: Chronic care: making the case for ongoing care (2010) Google Scholar 
  6. Apthorpe, N., Shvartzshnaider, Y., Mathur, A., Reisman, D., Feamster, N.: Discovering smart home internet of things privacy norms using contextual integrity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(2), 59:1–59:23 (2018). https://doi.org/10.1145/3214262
  7. Arruda, M.F., Bulcão-Neto, R.F.: Toward a lightweight ontology for privacy protection in IoT. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019, pp. 880–888. Association for Computing Machinery, New York, April 2019. https://doi.org/10.1145/3297280.3297367
  8. Ayobi, A., Marshall, P., Cox, A.L., Chen, Y.: Quantifying the body and caring for the mind: self-tracking in multiple sclerosis. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, Denver, Colorado, USA, pp. 6889–6901. Association for Computing Machinery, May 2017. https://doi.org/10.1145/3025453.3025869
  9. Bagalkot, N., Sokoler, T.: MagicMirror: towards enhancing collaborative rehabilitation practices. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, CSCW 2011, pp. 593–596. ACM, New York (2011). https://doi.org/10.1145/1958824.1958922. http://doi.acm.org/10.1145/1958824.1958922
  10. Bahirat, P., He, Y., Menon, A., Knijnenburg, B.: A data-driven approach to developing IoT privacy-setting interfaces. In: 23rd International Conference on Intelligent User Interfaces, IUI 2018, pp. 165–176. ACM, New York (2018). https://doi.org/10.1145/3172944.3172982. http://doi.acm.org/10.1145/3172944.3172982
  11. Barbosa, N.M., Park, J.S., Yao, Y., Wang, Y.: “What if?” predicting individual users’ smart home privacy preferences and their changes. Proc. Priv. Enhancing Technol. 2019(4), 211–231 (2019). https://doi.org/10.2478/popets-2019-0066
  12. Bernard, H.R.: Research Methods in Anthropology: Qualitative and Quantitative Approaches. Rowman & Littlefield (2017). Google-Books-ID: 2Fk7DwAAQBAJ Google Scholar 
  13. Bernard, H.R.: Social Research Methods: Qualitative and Quantitative Approaches. SAGE (2000). Google-Books-ID: VDPftmVO5lYC Google Scholar 
  14. Birnholtz, J., Jones-Rounds, M.: Independence and interaction: understanding seniors’ privacy and awareness needs for aging in place. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 143–152. ACM, New York (2010). https://doi.org/10.1145/1753326.1753349. http://doi.acm.org/10.1145/1753326.1753349
  15. Borders, K., Zhao, X., Prakash, A.: CPOL: high-performance policy evaluation. In: Proceedings of the 12th ACM conference on Computer and communications security, CCS 2005, pp. 147–157. Association for Computing Machinery, New York, November 2005. https://doi.org/10.1145/1102120.1102142
  16. Bowser, A., Shilton, K., Preece, J., Warrick, E.: Accounting for privacy in citizen science: ethical research in a context of openness. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, Portland, Oregon, USA, pp. 2124–2136. Association for Computing Machinery, February 2017. https://doi.org/10.1145/2998181.2998305
  17. Bowyer, A., Montague, K., Wheater, S., McGovern, R., Lingam, R., Balaam, M.: Understanding the family perspective on the storage, sharing and handling of family civic data. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, pp. 136:1–136:13. ACM, New York (2018). https://doi.org/10.1145/3173574.3173710. http://doi.acm.org/10.1145/3173574.3173710
  18. Bélanger, F., Crossler, R.E., Hiller, J.S., Park, J.M., Hsiao, M.S.: POCKET: a tool for protecting children’s privacy online. Decis. Support Syst. 54(2), 1161–1173 (2013). https://doi.org/10.1016/j.dss.2012.11.010. http://www.sciencedirect.com/science/article/pii/S0167923612003429
  19. Büyüktür, A.G., Ackerman, M.S., Newman, M.W., Hung, P.-Y.: Design considerations for semi-automated tracking: self-care plans in spinal cord injury. In: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2017, pp. 183–192. ACM, New York (2017). https://doi.org/10.1145/3154862.3154870. http://doi.acm.org/10.1145/3154862.3154870
  20. Büyüktür, A.G., Hung, P.-Y., Newman, M.W., Ackerman, M.S.: Supporting collaboratively constructed independence: a study of spinal cord injury. Proc. ACM Hum.-Comput. Interact. 2(CSCW), 26:1–26:25 (2018). https://doi.org/10.1145/3274295. http://doi.acm.org/10.1145/3274295
  21. Choe, E.K., Consolvo, S., Jung, J., Harrison, B., Kientz, J.A.: Living in a glass house: a survey of private moments in the home. In: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, Beijing, China, pp. 41–44. Association for Computing Machinery, September 2011. https://doi.org/10.1145/2030112.2030118
  22. Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A.: Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 1143–1152. ACM, New York (2014). https://doi.org/10.1145/2556288.2557372. http://doi.acm.org.proxy.lib.umich.edu/10.1145/2556288.2557372
  23. Chung, C.F., et al.: Identifying and planning for individualized change: patient-provider collaboration using lightweight food diaries in healthy eating and irritable bowel syndrome. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1), 7:1–7:27 (2019). https://doi.org/10.1145/3314394
  24. Clarke, A.: Situational Analysis: Grounded Theory After the Postmodern Turn. SAGE Publications, Thousand Oaks (2005)CrossRef  Google Scholar 
  25. Consolvo, S., Roessler, P., Shelton, B.E., LaMarca, A., Schilit, B., Bly, S.: Technology for care networks of elders. IEEE Pervasive Comput. 3(2), 22–29 (2004). https://doi.org/10.1109/MPRV.2004.1316814CrossRef  Google Scholar 
  26. Centers for Disease Control and Prevention: About Chronic Diseases—CDC (2020). https://www.cdc.gov/chronicdisease/about/index.htm
  27. Emami-Naeini, P., et al.: Privacy expectations and preferences in an IoT world. In: Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017), pp. 399–412. USENIX Association, Santa Clara (2017). https://www.usenix.org/conference/soups2017/technical-sessions/presentation/naeini
  28. Epstein, D.A., Borning, A., Fogarty, J.: Fine-grained sharing of sensed physical activity: a value sensitive approach. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013, pp. 489–498. ACM, New York (2013). https://doi.org/10.1145/2493432.2493433. http://doi.acm.org/10.1145/2493432.2493433
  29. Felipe, S., Singh, A., Bradley, C., Williams, A.C., Bianchi-Berthouze, N.: Roles for personal informatics in chronic pain. In: 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 161–168, May 2015. https://doi.org/10.4108/icst.pervasivehealth.2015.259501
  30. Figueiredo, M.C., Chen, Y.: Patient-generated health data: dimensions, challenges, and open questions. Found. Trends® Hum.-Comput. Interact. 13(3), 165–297 (2020). https://doi.org/10.1561/1100000080
  31. Fischer, G., Lemke, A.C., Mastaglio, T., Morch, A.I.: Using critics to empower users. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1990, pp. 337–347. ACM, New York (1990). https://doi.org/10.1145/97243.97305. http://doi.acm.org/10.1145/97243.97305
  32. Fong, P.W.: Relationship-based access control: protection model and policy language. In: Proceedings of the First ACM Conference on Data and Application Security and Privacy, CODASPY 2011, San Antonio, TX, USA, pp. 191–202. ACM, New York (2011). https://doi.org/10.1145/1943513.1943539. http://doi.acm.org/10.1145/1943513.1943539
  33. Glasgow, R.E., Anderson, R.M.: In diabetes care, moving from compliance to adherence is not enough. Diab. Care 22(12), 2090–2092 (1999)CrossRef  Google Scholar 
  34. Scientific Software Development GmbH: ATLAS.ti: The Qualitative Data Analysis & Research Software (2020). https://atlasti.com/
  35. Grönvall, E., Verdezoto, N.: Beyond self-monitoring: understanding non-functional aspects of home-based healthcare technology. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013, pp. 587–596. ACM, New York (2013). https://doi.org/10.1145/2493432.2493495
  36. Harbach, M., Hettig, M., Weber, S., Smith, M.: Using personal examples to improve risk communication for security & privacy decisions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 2647–2656. ACM, New York (2014). https://doi.org/10.1145/2556288.2556978. http://doi.acm.org/10.1145/2556288.2556978
  37. He, Y., Bahirat, P., Knijnenburg, B.P., Menon, A.: A data-driven approach to designing for privacy in household IoT. ACM Trans. Interact. Intell. Syst. (TiiS) 10(1), 10:1–10:47 (2019). https://doi.org/10.1145/3241378
  38. Hong, M.K., Wilcox, L., Machado, D., Olson, T.A., Simoneaux, S.F.: Care partnerships: toward technology to support teens’ participation in their health care. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 5337–5349. ACM, New York (2016). https://doi.org/10.1145/2858036.2858508. http://doi.acm.org/10.1145/2858036.2858508
  39. Hung, P.-Y., Ackerman, M.S.: Discount expertise metrics for augmenting community interaction. In: Proceedings of the Work-In-Progress Track of the 7th International Conference on Communities and Technologies, vol. 12, pp. 43–52 (2015) Google Scholar 
  40. Iachello, G., et al.: Control, deception, and communication: evaluating the deployment of a location-enhanced messaging service. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 213–231. Springer, Heidelberg (2005). https://doi.org/10.1007/11551201_13CrossRef  Google Scholar 
  41. Jacobs, M.L., Clawson, J., Mynatt, E.D.: Comparing health information sharing preferences of cancer patients, doctors, and navigators. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2015, pp. 808–818. ACM, New York (2015). https://doi.org/10.1145/2675133.2675252. http://doi.acm.org/10.1145/2675133.2675252
  42. Junior, M.P., Xavier, S.I.D.R., Prates, R.O.: Investigating the use of a simulator to support users in anticipating impact of privacy settings in Facebook. In: Proceedings of the 18th International Conference on Supporting Group Work, GROUP 2014, pp. 63–72. ACM, New York (2014). https://doi.org/10.1145/2660398.2660419. http://doi.acm.org/10.1145/2660398.2660419
  43. Kanaan, H., Mahmood, K., Sathyan, V.: An ontological model for privacy in emerging decentralized healthcare systems. In: 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS), pp. 107–113, March 2017. https://doi.org/10.1109/ISADS.2017.37
  44. Kariotis, T., et al.: Emerging health data platforms: from individual control to collective data governance. Data Policy 2 (2020). https://doi.org/10.1017/dap.2020.14
  45. Karkar, R., et al.: TummyTrials: a feasibility study of using self-experimentation to detect individualized food triggers. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, pp. 6850–6863. ACM, New York (2017). https://doi.org/10.1145/3025453.3025480. http://doi.acm.org/10.1145/3025453.3025480
  46. Kelley, P.G., Cranor, L.F., Sadeh, N.: Privacy as part of the app decision-making process. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 3393–3402. ACM, New York (2013). https://doi.org/10.1145/2470654.2466466. http://doi.acm.org/10.1145/2470654.2466466
  47. Knijnenburg, B.P.: Information disclosure profiles for segmentation and recommendation. In: SOUPS2014 Workshop on Privacy Personas and Segmentation (2014) Google Scholar 
  48. Kumar, P., Schoenebeck, S.: The modern day baby book: enacting good mothering and stewarding privacy on Facebook. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2015, Vancouver, BC, Canada, pp. 1302–1312. Association for Computing Machinery, February 2015. https://doi.org/10.1145/2675133.2675149
  49. Kumaraguru, P., Cranor, L., Lobo, J., Calo, S.: A survey of privacy policy languages. In: Workshop on Usable IT Security Management (USM 2007): Proceedings of the 3rd Symposium on Usable Privacy and Security. ACM (2007) Google Scholar 
  50. Könings, B.: User-centered awareness and control of privacy in Ubiquitous Computing. Dissertation, Universität Ulm, July 2015. https://doi.org/10.18725/OPARU-3240. https://oparu.uni-ulm.de/xmlui/handle/123456789/3267
  51. Langheinrich, M.: Privacy by design—principles of privacy-aware ubiquitous systems. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 273–291. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45427-6_23CrossRef  MATH  Google Scholar 
  52. Lee, H., Kobsa, A.: Privacy preference modeling and prediction in a simulated campuswide IoT environment. In: 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 276–285, March 2017. https://doi.org/10.1109/PERCOM.2017.7917874. ISSN 2474-249X
  53. Li, I., Dey, A., Forlizzi, J.: A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA, 10–15 April 2010, CHI 2010, pp. 557–566. ACM, New York (2010). https://doi.org/10.1145/1753326.1753409. http://doi.acm.org.proxy.lib.umich.edu/10.1145/1753326.1753409
  54. Li, Y., Vishwamitra, N., Hu, H., Caine, K.: Towards a taxonomy of content sensitivity and sharing preferences for photos. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI 2020, pp. 1–14. Association for Computing Machinery, New York, April 2020. https://doi.org/10.1145/3313831.3376498
  55. Liu, L.S., et al.: Improving communication and social support for caregivers of high-risk infants through mobile technologies. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, CSCW 2011, pp. 475–484. ACM, New York (2011). https://doi.org/10.1145/1958824.1958897. http://doi.acm.org/10.1145/1958824.1958897
  56. Liu, Y., Gummadi, K.P., Krishnamurthy, B., Mislove, A.: Analyzing Facebook privacy settings: user expectations vs. reality. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 61–70 (2011) Google Scholar 
  57. Loukil, F., Ghedira-Guegan, C., Boukadi, K., Benharkat, A.N.: LIoPY: a legal compliant ontology to preserve privacy for the Internet of Things. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, pp. 701–706, July 2018. https://doi.org/10.1109/COMPSAC.2018.10322. ISSN 0730-3157
  58. Luca, A.D., Zezschwitz, E.V.: Usable privacy and security. IT – Inf. Technol. 58(5), 215–216 (2016). https://doi.org/10.1515/itit-2016-0034. https://www.degruyter.com/document/doi/10.1515/itit-2016-0034/html
  59. Mamykina, L., Mynatt, E., Davidson, P., Greenblatt, D.: MAHI: investigation of social scaffolding for reflective thinking in diabetes management. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 477–486. ACM, New York (2008). https://doi.org/10.1145/1357054.1357131. http://doi.acm.org/10.1145/1357054.1357131
  60. Mazzia, A., LeFevre, K., Adar, E.: The PViz comprehension tool for social network privacy settings. In: Proceedings of the Eighth Symposium on Usable Privacy and Security, SOUPS 2012, pp. 13:1–13:12. ACM, New York (2012). https://doi.org/10.1145/2335356.2335374. http://doi.acm.org/10.1145/2335356.2335374
  61. Meade, M.A.: Health Mechanics: Tools for the Self-Management of Spinal Cord Injury and Disease. University of Michigan, Ann Arbor (2009) Google Scholar 
  62. Miro: Miro—Free Online Collaborative Whiteboard Platform (2020). https://miro.com/
  63. Murnane, E.L., Walker, T.G., Tench, B., Voida, S., Snyder, J.: Personal informatics in interpersonal contexts: towards the design of technology that supports the social ecologies of long-term mental health management. Proc. ACM Hum.-Comput. Interact. 2(CSCW), 127:1–127:27 (2018). https://doi.org/10.1145/3274396
  64. Nissen, B., et al.: Should I Agree?: delegating consent decisions beyond the individual. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland UK, pp. 515:1–515:13. ACM, New York (2019). https://doi.org/10.1145/3290605.3300745. http://doi.acm.org/10.1145/3290605.3300745
  65. Nunes, F., Fitzpatrick, G.: Self-care technologies and collaboration. Int. J. Hum.-Comput. Interact. 31(12), 869–881 (2015). https://doi.org/10.1080/10447318.2015.1067498
  66. Odom, W., Sellen, A., Harper, R., Thereska, E.: Lost in translation: understanding the possession of digital things in the cloud. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2012, Austin, Texas, USA, pp. 781–790. Association for Computing Machinery, May 2012. https://doi.org/10.1145/2207676.2207789
  67. Olejnik, K., Dacosta, I., Machado, J.S., Huguenin, K., Khan, M.E., Hubaux, J.P.: SmarPer: context-aware and automatic runtime-permissions for mobile devices. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 1058–1076, May 2017. https://doi.org/10.1109/SP.2017.25. ISSN 2375-1207
  68. World Health Organization: WHO—Integrated chronic disease prevention and control (2020). https://www.who.int/chp/about/integrated_cd/en/
  69. Pina, L.R., et al.: From personal informatics to family informatics: understanding family practices around health monitoring. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, pp. 2300–2315. ACM, New York (2017). https://doi.org/10.1145/2998181.2998362. http://doi.acm.org/10.1145/2998181.2998362
  70. Piras, E.M.: Beyond self-tracking: exploring and unpacking four emerging labels of patient data work. Health Inform. J. 25(3), 598–607 (2019). https://doi.org/10.1177/1460458219833121CrossRef  Google Scholar 
  71. Raber, F., Luca, A.D., Graus, M.: Privacy wedges: area-based audience selection for social network posts. In: Twelfth Symposium on Usable Privacy and Security (SOUPS 2016). USENIX Association, Denver (2016). https://www.usenix.org/conference/soups2016/workshop-program/wpi/presentation/raber
  72. Reeder, R.W., et al.: Expandable grids for visualizing and authoring computer security policies. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 1473–1482. ACM, New York (2008). https://doi.org/10.1145/1357054.1357285. http://doi.acm.org/10.1145/1357054.1357285
  73. Sandhu, R.S., Coyne, E.J., Feinstein, H.L., Youman, C.E.: Role-based access control models. Computer 29(2), 38–47 (1996). https://doi.org/10.1109/2.485845CrossRef  Google Scholar 
  74. Schaub, F., Könings, B., Lang, P., Wiedersheim, B., Winkler, C., Weber, M.: PriCal: context-adaptive privacy in ambient calendar displays. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014, pp. 499–510. ACM, New York (2014). https://doi.org/10.1145/2632048.2632087. http://doi.acm.org/10.1145/2632048.2632087
  75. Schroeder, J., et al.: Examining self-tracking by people with migraine: goals, needs, and opportunities in a chronic health condition. In: Proceedings of the 2018 Designing Interactive Systems Conference, DIS 2018, Hong Kong, China, pp. 135–148. Association for Computing Machinery, June 2018. https://doi.org/10.1145/3196709.3196738
  76. Schroeder, J., Hoffswell, J., Chung, C.F., Fogarty, J., Munson, S., Zia, J.: Supporting patient-provider collaboration to identify individual triggers using food and symptom journals. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, pp. 1726–1739. ACM, New York (2017). https://doi.org/10.1145/2998181.2998276. http://doi.acm.org/10.1145/2998181.2998276
  77. Quantified Self: Quantified Self – Self Knowledge Through Numbers. https://quantifiedself.com/, library Catalog: quantifiedself.com
  78. Sharma, A., Cosley, D.: Studying and modeling the connection between people’s preferences and content sharing. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2015, Vancouver, BC, Canada, pp. 1246–1257. Association for Computing Machinery, February 2015. https://doi.org/10.1145/2675133.2675151
  79. Smullen, D., Feng, Y., Zhang, S.A., Sadeh, N.: The best of both worlds: mitigating trade-offs between accuracy and user burden in capturing mobile app privacy preferences. Proc. Priv. Enhancing Technol. 2020(1), 195–215 (2020). https://doi.org/10.2478/popets-2020-0011. https://content.sciendo.com/view/journals/popets/2020/1/article-p195.xml
  80. Suh, J., Williams, S., Fann, J.R., Fogarty, J., Bauer, A.M., Hsieh, G.: Parallel journeys of patients with cancer and depression: challenges and opportunities for technology-enabled collaborative care. Proc. ACM Hum.-Comput. Interact. 4(CSCW1), 038:1–038:36 (2020). https://doi.org/10.1145/3392843
  81. Thayer, A., Bietz, M.J., Derthick, K., Lee, C.P.: I love you, let’s share calendars: calendar sharing as relationship work. In: Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, CSCW 2012, Seattle, Washington, USA, pp. 749–758. Association for Computing Machinery, February 2012. https://doi.org/10.1145/2145204.2145317. https://doi.org/10.1145/2145204.2145317
  82. Toch, E., et al.: Empirical models of privacy in location sharing. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, UbiComp 2010, pp. 129–138. Association for Computing Machinery, New York, September 2010. https://doi.org/10.1145/1864349.1864364
  83. Toch, E., et al.: Locaccino: a privacy-centric location sharing application. In: Proceedings of the 12th ACM International Conference Adjunct Papers on Ubiquitous Computing – Adjunct, UbiComp 2010, pp. 381–382. Adjunct, ACM, New York (2010). https://doi.org/10.1145/1864431.1864446. http://doi.acm.org/10.1145/1864431.1864446
  84. Tolmie, P., Crabtree, A., Rodden, T., Colley, J., Luger, E.: “This has to be the cats”: personal data legibility in networked sensing systems. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW 2016, pp. 491–502. ACM, New York (2016). https://doi.org/10.1145/2818048.2819992. http://doi.acm.org/10.1145/2818048.2819992
  85. Tsai, L., et al.: Turtle guard: helping android users apply contextual privacy preferences. In: Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017), pp. 145–162. USENIX Association, Santa Clara (2017). https://www.usenix.org/conference/soups2017/technical-sessions/presentation/tsai
  86. Van Kleek, M., Liccardi, I., Binns, R., Zhao, J., Weitzner, D.J., Shadbolt, N.: Better the devil you know: exposing the data sharing practices of smartphone apps. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, pp. 5208–5220. ACM, New York (2017). https://doi.org/10.1145/3025453.3025556. http://doi.acm.org/10.1145/3025453.3025556
  87. Vertesi, J., Kaye, J., Jarosewski, S.N., Khovanskaya, V.D., Song, J.: Data narratives: uncovering tensions in personal data management. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW 2016, pp. 478–490. ACM, New York (2016). https://doi.org/10.1145/2818048.2820017. http://doi.acm.org/10.1145/2818048.2820017
  88. Vescovi, M., Perentis, C., Leonardi, C., Lepri, B., Moiso, C.: My data store: toward user awareness and control on personal data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp 2014, pp. 179–182. Adjunct, ACM, New York (2014). https://doi.org/10.1145/2638728.2638745. http://doi.acm.org/10.1145/2638728.2638745
  89. Voida, A., Grinter, R.E., Ducheneaut, N., Edwards, W.K., Newman, M.W.: Listening in: practices surrounding iTunes music sharing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2005, Portland, Oregon, USA, pp. 191–200. Association for Computing Machinery, April 2005. https://doi.org/10.1145/1054972.1054999
  90. Wang, S., Hou, Y., Gao, F., Ma, S.: Ontology-based resource description model for Internet of Things. In: 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 105–108, October 2016. https://doi.org/10.1109/CyberC.2016.29
  91. Wang, Y., Gou, L., Xu, A., Zhou, M.X., Yang, H., Badenes, H.: VeilMe: an interactive visualization tool for privacy configuration of using personality traits. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 817–826. ACM (2015) Google Scholar 
  92. Wang, Y., Leon, P.G., Acquisti, A., Cranor, L.F., Forget, A., Sadeh, N.: A field trial of privacy nudges for Facebook. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 2367–2376. ACM, New York (2014). https://doi.org/10.1145/2556288.2557413. http://doi.acm.org/10.1145/2556288.2557413
  93. Westin, A.F.: Privacy and Freedom (1968). Google-Books-ID: EqGAfBTQreMC Google Scholar 
  94. Yu, S.H., et al.: A mobile mediation tool for improving interaction between depressed individuals and caregivers. Pers. Ubiquitous Comput. 15(7), 695–706 (2011). https://doi.org/10.1007/s00779-010-0347-zCrossRef  Google Scholar 
  95. Zhao, J., Binns, R., Van Kleek, M., Shadbolt, N.: Privacy languages: are we there yet to enable user controls? In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016, pp. 799–806. Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, April 2016. https://doi.org/10.1145/2872518.2890590
  96. Zoom: Video Conferencing, Web Conferencing, Webinars, Screen Sharing (2020). https://zoom.us/
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