{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T18:09:48Z","timestamp":1774634988916,"version":"3.50.1"},"reference-count":100,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"2","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cameras are everywhere, and are increasingly coupled with video analytics software that can identify our face, track our mood, recognize what we are doing, and more. We present the results of a 10-day in-situ study designed to understand how people feel about these capabilities, looking both at the extent to which they expect to encounter them as part of their everyday activities and at how comfortable they are with the presence of such technologies across a range of realistic scenarios. Results indicate that while some widespread deployments are expected by many (e.g., surveillance in public spaces), others are not, with some making people feel particularly uncomfortable. Our results further show that individuals\u2019 privacy preferences and expectations are complicated and vary with a number of factors such as the purpose for which footage is captured and analyzed, the particular venue where it is captured, and whom it is shared with. Finally, we discuss the implications of people\u2019s rich and diverse preferences on opt-in or opt-out rights for the collection and use (including sharing) of data associated with these video analytics scenarios as mandated by regulations. Because of the user burden associated with the large number of privacy decisions people could be faced with, we discuss how new types of privacy assistants could possibly be configured to help people manage these decisions.<\/jats:p>","DOI":"10.2478\/popets-2021-0028","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T21:11:04Z","timestamp":1617743464000},"page":"282-304","source":"Crossref","is-referenced-by-count":24,"title":["\u201cDid you know this camera tracks your mood?\u201d: Understanding Privacy Expectations and Preferences in the Age of Video Analytics"],"prefix":"10.56553","volume":"2021","author":[{"given":"Shikun","family":"Zhang","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Yuanyuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Lujo","family":"Bauer","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Lorrie Faith","family":"Cranor","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Anupam","family":"Das","sequence":"additional","affiliation":[{"name":"North Carolina State University"}]},{"given":"Norman","family":"Sadeh","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]}],"member":"35752","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"2022050115345152983_j_popets-2021-0028_ref_001_w2aab3b7c30b1b6b1ab1ab1Aa","unstructured":"[1] Augmented mental health: Revolutionary mental health care using emotion recognition. https:\/\/www.augmentedmentalhealth.com\/blog\/augmented-mental-health-revolutionary-mental-health-care-using-emotion-recognition, May 2018. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_002_w2aab3b7c30b1b6b1ab1ab2Aa","unstructured":"[2] Chinese man caught by facial recognition at pop concert. https:\/\/www.bbc.com\/news\/world-asia-china-43751276, April 2018. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_003_w2aab3b7c30b1b6b1ab1ab3Aa","unstructured":"[3] Facial recognition: School ID checks lead to GDPR fine. https:\/\/www.bbc.com\/news\/technology-49489154, August 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_004_w2aab3b7c30b1b6b1ab1ab4Aa","unstructured":"[4] Facial recognition technology: Ensuring transparency in government use. https:\/\/www.nist.gov\/speech-testimony\/facial-recognition-technology-ensuring-transparency-government-use, June 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_005_w2aab3b7c30b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] A. Acquisti, I. Adjerid, R. Balebako, L. Brandimarte, L. F. Cranor, S. Komanduri, P. G. Leon, N. Sadeh, F. Schaub, M. Sleeper, et al. Nudges for privacy and security: Understanding and assisting users\u2019 choices online. ACM Computing Surveys (CSUR), 50(3):1\u201341, 2017.","DOI":"10.1145\/3054926"},{"key":"2022050115345152983_j_popets-2021-0028_ref_006_w2aab3b7c30b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] A. Acquisti and R. Gross. Imagined communities: Awareness, information sharing, and privacy on the facebook. In International Workshop on Privacy Enhancing Technologies, pages 36\u201358. Springer, 2006.10.1007\/11957454_3","DOI":"10.1007\/11957454_3"},{"key":"2022050115345152983_j_popets-2021-0028_ref_007_w2aab3b7c30b1b6b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] A. Acquisti and J. Grossklags. Privacy and rationality in individual decision making. IEEE Security & Privacy, 3(1):26\u201333, 2005.10.1109\/MSP.2005.22","DOI":"10.1109\/MSP.2005.22"},{"key":"2022050115345152983_j_popets-2021-0028_ref_008_w2aab3b7c30b1b6b1ab1ab8Aa","unstructured":"[8] M. Allen. Health insurers are vacuuming up details about you \u2014 and it could raise your rates. https:\/\/www.propublica.org\/article\/health-insurers-are-vacuuming-up-details-about-you-and-it-could-raise-your-rates, July 2018. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_009_w2aab3b7c30b1b6b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[9] H. Almuhimedi, F. Schaub, N. Sadeh, I. Adjerid, A. Acquisti, J. Gluck, L. F. Cranor, and Y. Agarwal. 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 \u201915), pages 787\u2013796, 2015.10.1145\/2702123.2702210","DOI":"10.1145\/2702123.2702210"},{"key":"2022050115345152983_j_popets-2021-0028_ref_010_w2aab3b7c30b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] N. Apthorpe, Y. Shvartzshnaider, A. Mathur, D. Reisman, and N. Feamster. Discovering smart home internet of things privacy norms using contextual integrity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2(2), July 2018.10.1145\/3214262","DOI":"10.1145\/3214262"},{"key":"2022050115345152983_j_popets-2021-0028_ref_011_w2aab3b7c30b1b6b1ab1ac11Aa","unstructured":"[11] R. Bachman. Your gym\u2019s tech wants to know you better. https:\/\/www.wsj.com\/articles\/your-gyms-tech-wants-to-know-you-better-1497281915, June 2017. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_012_w2aab3b7c30b1b6b1ab1ac12Aa","unstructured":"[12] S. P. Bailey. Skipping church? Facial recognition software could be tracking you. http:\/\/www.washingtonpost.com\/news\/acts-of-faith\/wp\/2015\/07\/24\/skipping-church-facial-recognition-software-could-be-tracking-you\/, July 2015. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_013_w2aab3b7c30b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] L. F. Barrett and D. J. Barrett. An introduction to computerized experience sampling in psychology. Social Science Computer Review, 19(2):175\u2013185, 2001.10.1177\/089443930101900204","DOI":"10.1177\/089443930101900204"},{"key":"2022050115345152983_j_popets-2021-0028_ref_014_w2aab3b7c30b1b6b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] D. Bates, M. M\u00e4chler, B. Bolker, and S. Walker. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1):1\u201348, 2015.10.18637\/jss.v067.i01","DOI":"10.18637\/jss.v067.i01"},{"key":"2022050115345152983_j_popets-2021-0028_ref_015_w2aab3b7c30b1b6b1ab1ac15Aa","unstructured":"[15] Bloomberg News. Mannequins collect data on shoppers via facial-recognition software. https:\/\/www.washingtonpost.com\/business\/economy\/mannequins-collect-data-on-shoppers-via-facial-recognition-software\/2012\/11\/22\/0751b992-3425-11e2-9cfa-e41bac906cc9_story.html, November 2012. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_016_w2aab3b7c30b1b6b1ab1ac16Aa","unstructured":"[16] D. Burrows. Facial expressions show Mars the adverts that will drive sales. https:\/\/www.foodnavigator.com\/Article\/2017\/03\/23\/Facial-expressions-show-Mars-the-adverts-that-will-drive-sales, May 2017. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_017_w2aab3b7c30b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] L. L. Carstensen, B. Turan, S. Scheibe, N. Ram, H. Ersner-Hershfield, G. R. Samanez-Larkin, K. P. Brooks, and J. R. Nesselroade. Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26(1):21, 2011.","DOI":"10.1037\/a0021285"},{"key":"2022050115345152983_j_popets-2021-0028_ref_018_w2aab3b7c30b1b6b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[18] R. Chow. The last mile for IoT privacy. IEEE Security & Privacy, 15(6):73\u201376, 2017.10.1109\/MSP.2017.4251118","DOI":"10.1109\/MSP.2017.4251118"},{"key":"2022050115345152983_j_popets-2021-0028_ref_019_w2aab3b7c30b1b6b1ab1ac19Aa","unstructured":"[19] R. H. B. Christensen. ordinal\u2014regression models for ordinal data, 2019. R package version 2019.12-10. https:\/\/CRAN.R-project.org\/package=ordinal."},{"key":"2022050115345152983_j_popets-2021-0028_ref_020_w2aab3b7c30b1b6b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] T. C. Christensen, L. F. Barrett, E. Bliss-Moreau, K. Lebo, and C. Kaschub. A practical guide to experience-sampling procedures. Journal of Happiness Studies, 4(1):53\u201378, 2003.10.1023\/A:1023609306024","DOI":"10.1023\/A:1023609306024"},{"key":"2022050115345152983_j_popets-2021-0028_ref_021_w2aab3b7c30b1b6b1ab1ac21Aa","unstructured":"[21] L. Clark. Mannequins are spying on shoppers for market analysis. https:\/\/www.wired.co.uk\/article\/mannequin-spies-on-customers, November 2012. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_022_w2aab3b7c30b1b6b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] J. Colnago, Y. Feng, T. Palanivel, S. Pearman, M. Ung, A. Acquisti, L. F. Cranor, and N. Sadeh. Informing the design of a personalized privacy assistant for the internet of things. In Proceedings of the 2020 CHI Conference on Human Factors in Computing System (CHI \u201920), pages 1\u201313, 2020.10.1145\/3313831.3376389","DOI":"10.1145\/3313831.3376389"},{"key":"2022050115345152983_j_popets-2021-0028_ref_023_w2aab3b7c30b1b6b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] J. Colnago and H. Guardia. How to inform privacy agents on preferred level of user control? In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp \u201916), pages 1542\u20131547, 2016.10.1145\/2968219.2968546","DOI":"10.1145\/2968219.2968546"},{"key":"2022050115345152983_j_popets-2021-0028_ref_024_w2aab3b7c30b1b6b1ab1ac24Aa","unstructured":"[24] B. Conarck. Florida court: Prosecutors had no obligation to turn over facial recognition evidence. https:\/\/www.jacksonville.com\/news\/20190123\/florida-court-prosecutorshad-no-obligation-to-turn-over-facial-recognition-evidence, January 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_025_w2aab3b7c30b1b6b1ab1ac25Aa","unstructured":"[25] K. Conger, R. Fausset, and S. F. Kovaleski. San Francisco bans facial recognition technology. https:\/\/www.nytimes.com\/2019\/05\/14\/us\/facial-recognition-ban-san-francisco.html, May 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_026_w2aab3b7c30b1b6b1ab1ac26Aa","doi-asserted-by":"crossref","unstructured":"[26] S. Consolvo and M. Walker. Using the experience sampling method to evaluate ubicomp applications. IEEE Pervasive Computing, 2(2):24\u201331, 2003.10.1109\/MPRV.2003.1203750","DOI":"10.1109\/MPRV.2003.1203750"},{"key":"2022050115345152983_j_popets-2021-0028_ref_027_w2aab3b7c30b1b6b1ab1ac27Aa","doi-asserted-by":"crossref","unstructured":"[27] A. Das, M. Degeling, D. Smullen, and N. Sadeh. Personalized privacy assistants for the Internet of Things: Providing users with notice and choice. IEEE Pervasive Computing, 17(3):35\u201346, 2018.","DOI":"10.1109\/MPRV.2018.03367733"},{"key":"2022050115345152983_j_popets-2021-0028_ref_028_w2aab3b7c30b1b6b1ab1ac28Aa","unstructured":"[28] B. J. Davidson. How your business can benefit from facial recognition technology. https:\/\/percentotech.com\/how-your-business-can-benefit-from-facial-recognition-technology\/, November 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_029_w2aab3b7c30b1b6b1ab1ac29Aa","unstructured":"[29] D. DeChiaro. New York City eyes regulation of facial recognition technology. https:\/\/www.rollcall.com\/news\/congress\/new-york-city-eyes-regulation-of-facial-recognition-technology, October 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_030_w2aab3b7c30b1b6b1ab1ac30Aa","doi-asserted-by":"crossref","unstructured":"[30] B. Djellali, K. Belarbi, A. Chouarfia, and P. Lorenz. User authentication scheme preserving anonymity for ubiquitous devices. Security and Communication Networks, 8(17):3131\u20133141, 2015.10.1002\/sec.1238","DOI":"10.1002\/sec.1238"},{"key":"2022050115345152983_j_popets-2021-0028_ref_031_w2aab3b7c30b1b6b1ab1ac31Aa","doi-asserted-by":"crossref","unstructured":"[31] Y. Duan and J. Canny. Protecting user data in ubiquitous computing: Towards trustworthy environments. In International Workshop on Privacy Enhancing Technologies, pages 167\u2013185. Springer, 2004.10.1007\/11423409_11","DOI":"10.1007\/11423409_11"},{"key":"2022050115345152983_j_popets-2021-0028_ref_032_w2aab3b7c30b1b6b1ab1ac32Aa","unstructured":"[32] M. Ehrenkranz. Burger joint teams up with surveillance giant to scan your face for loyalty points. https:\/\/gizmodo.com\/burger-joint-teams-up-with-surveillance-giant-to-scan-y-1821498988, December 2017. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_033_w2aab3b7c30b1b6b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"[33] M. Elkhodr, S. Shahrestani, and H. Cheung. A contextual-adaptive location disclosure agent for general devices in the internet of things. In 38th Annual IEEE Conference on Local Computer Networks - Workshops, pages 848\u2013855. IEEE, 2013.10.1109\/LCNW.2013.6758522","DOI":"10.1109\/LCNW.2013.6758522"},{"key":"2022050115345152983_j_popets-2021-0028_ref_034_w2aab3b7c30b1b6b1ab1ac34Aa","unstructured":"[34] D. Etherington. Baidu and KFC\u2019s new smart restaurant suggests what to order based on your face. https:\/\/techcrunch.com\/2016\/12\/23\/baidu-and-kfcs-new-smart-restaurant-suggests-what-to-order-based-on-your-face\/, December 2016. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_035_w2aab3b7c30b1b6b1ab1ac35Aa","unstructured":"[35] I. Fadelli. Analyzing spoken language and 3-D facial expressions to measure depression severity. https:\/\/techxplore.com\/news\/2018-11-spoken-language-d-facial-depression.html, December 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_036_w2aab3b7c30b1b6b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[36] D. Ferreira, J. Goncalves, V. Kostakos, L. Barkhuus, and A. K. Dey. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th International Conference on Human-computer Interaction with Mobile Devices & Services (MobileHCI \u201914), pages 91\u2013100, 2014.10.1145\/2628363.2628367","DOI":"10.1145\/2628363.2628367"},{"key":"2022050115345152983_j_popets-2021-0028_ref_037_w2aab3b7c30b1b6b1ab1ac37Aa","unstructured":"[37] C. Frey. Revealed: how facial recognition has invaded shops\u2014and your privacy. https:\/\/www.theguardian.com\/cities\/2016\/mar\/03\/revealed-facial-recognition-software-infiltrating-cities-saks-toronto, March 2016. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_038_w2aab3b7c30b1b6b1ab1ac38Aa","unstructured":"[38] S. F. Gale. Employers turn to biometric technology to track attendance. https:\/\/www.workforce.com\/news\/employers-turn-to-biometric-technology-to-track-attendance, March 2013. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_039_w2aab3b7c30b1b6b1ab1ac39Aa","doi-asserted-by":"crossref","unstructured":"[39] P. Grother, M. Ngan, and K. Hanaoka. Ongoing face recognition vendor test (FRVT) part 2: Identification. https:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2018\/NIST.IR.8238.pdf, November 2018. Accessed: 2020-12-15.","DOI":"10.6028\/NIST.IR.8238"},{"key":"2022050115345152983_j_popets-2021-0028_ref_040_w2aab3b7c30b1b6b1ab1ac40Aa","doi-asserted-by":"crossref","unstructured":"[40] P. Grother, M. Ngan, and K. Hanaoka. Face recognition vendor test (FRVT) part 3: Demographic effects. http:\/\/nvlpubs.nist.gov\/nistpubs\/ir\/2019\/NIST.IR.8280.pdf, December 2019. Accessed: 2020-12-15.","DOI":"10.6028\/NIST.IR.8280"},{"key":"2022050115345152983_j_popets-2021-0028_ref_041_w2aab3b7c30b1b6b1ab1ac41Aa","doi-asserted-by":"crossref","unstructured":"[41] Y. Gurovich, Y. Hanani, O. Bar, G. Nadav, N. Fleischer, D. Gelbman, L. Basel-Salmon, P. M. Krawitz, S. B. Kamphausen, M. Zenker, L. M. Bird, and K. W. Gripp. Identifying facial phenotypes of genetic disorders using deep learning. Nature Medicine, 25(1):60\u201364, 2019.10.1038\/s41591-018-0279-030617323","DOI":"10.1038\/s41591-018-0279-0"},{"key":"2022050115345152983_j_popets-2021-0028_ref_042_w2aab3b7c30b1b6b1ab1ac42Aa","doi-asserted-by":"crossref","unstructured":"[42] J. M. Hektner, J. A. Schmidt, and M. Csikszentmihalyi. Experience sampling method: Measuring the quality of everyday life. Sage, 2007.10.4135\/9781412984201","DOI":"10.4135\/9781412984201"},{"key":"2022050115345152983_j_popets-2021-0028_ref_043_w2aab3b7c30b1b6b1ab1ac43Aa","doi-asserted-by":"crossref","unstructured":"[43] W. Hofmann, R. F. Baumeister, G. F\u00f6rster, and K. D. Vohs. Everyday temptations: An experience sampling study of desire, conflict, and self-control. Journal of Personality and Social Psychology, 102(6):1318, 2012.","DOI":"10.1037\/a0026545"},{"key":"2022050115345152983_j_popets-2021-0028_ref_044_w2aab3b7c30b1b6b1ab1ac44Aa","doi-asserted-by":"crossref","unstructured":"[44] J. I. Hong and J. A. Landay. An architecture for privacy-sensitive ubiquitous computing. In Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services (MobiSys \u201904), pages 177\u2013189, 2004.10.1145\/990064.990087","DOI":"10.1145\/990064.990087"},{"key":"2022050115345152983_j_popets-2021-0028_ref_045_w2aab3b7c30b1b6b1ab1ac45Aa","unstructured":"[45] T. Johnson. Shoplifters meet their match as retailers deploy facial recognition cameras. https:\/\/www.mcclatchydc.com\/news\/nation-world\/national\/article211455924.html, May 2018. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_046_w2aab3b7c30b1b6b1ab1ac46Aa","doi-asserted-by":"crossref","unstructured":"[46] E. Kanjo, L. Al-Husain, and A. Chamberlain. Emotions in context: examining pervasive affective sensing systems, applications, and analyses. Personal and Ubiquitous Computing, 19(7):1197\u20131212, 2015.","DOI":"10.1007\/s00779-015-0842-3"},{"key":"2022050115345152983_j_popets-2021-0028_ref_047_w2aab3b7c30b1b6b1ab1ac47Aa","doi-asserted-by":"crossref","unstructured":"[47] D. Korgut and D. F. Pigatto. An internet of things-based house monitoring system. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 01149\u201301152, June 2018.10.1109\/ISCC.2018.8538680","DOI":"10.1109\/ISCC.2018.8538680"},{"key":"2022050115345152983_j_popets-2021-0028_ref_048_w2aab3b7c30b1b6b1ab1ac48Aa","doi-asserted-by":"crossref","unstructured":"[48] I. Kramer et al. A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial. World Psychiatry, 13(1):68\u201377, 2014.","DOI":"10.1002\/wps.20090"},{"key":"2022050115345152983_j_popets-2021-0028_ref_049_w2aab3b7c30b1b6b1ab1ac49Aa","unstructured":"[49] S. Krouse. The new ways your boss is spying on you. https:\/\/www.wsj.com\/articles\/the-new-ways-your-boss-is-spying-on-you-11563528604, July 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_050_w2aab3b7c30b1b6b1ab1ac50Aa","doi-asserted-by":"crossref","unstructured":"[50] J. Kruger and D. Dunning. Unskilled and unaware of it: How difficulties in recognizing one\u2019s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6):1121\u20131134, 1999.","DOI":"10.1037\/0022-3514.77.6.1121"},{"key":"2022050115345152983_j_popets-2021-0028_ref_051_w2aab3b7c30b1b6b1ab1ac51Aa","doi-asserted-by":"crossref","unstructured":"[51] H. Lee and A. Kobsa. Understanding user privacy in internet of things environments. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pages 407\u2013412, 2016.10.1109\/WF-IoT.2016.7845392","DOI":"10.1109\/WF-IoT.2016.7845392"},{"key":"2022050115345152983_j_popets-2021-0028_ref_052_w2aab3b7c30b1b6b1ab1ac52Aa","doi-asserted-by":"crossref","unstructured":"[52] H. Lee and A. Kobsa. Privacy preference modeling and prediction in a simulated campuswide IoT environment. In 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 276\u2013285, 2017.10.1109\/PERCOM.2017.7917874","DOI":"10.1109\/PERCOM.2017.7917874"},{"key":"2022050115345152983_j_popets-2021-0028_ref_053_w2aab3b7c30b1b6b1ab1ac53Aa","unstructured":"[53] S. Lepitak. Disney\u2019s Dumbo and Accenture Interactive collaborate for the movie poster of the future. https:\/\/www.thedrum.com\/news\/2019\/03\/10\/disneys-dumbo-andaccenture-interactive-collaborate-the-movie-poster-the-future, March 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_054_w2aab3b7c30b1b6b1ab1ac54Aa","unstructured":"[54] D. Levine. What high-tech tools are available to fight depression? https:\/\/health.usnews.com\/health-care\/patient-advice\/articles\/2017-10-06\/what-high-tech-tools-are-available-to-fight-depression, October 2017. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_055_w2aab3b7c30b1b6b1ab1ac55Aa","unstructured":"[55] D. Levine. What your face may tell lenders about whether you\u2019re creditworthy. https:\/\/www.wsj.com\/articles\/what-your-face-may-tell-lenders-about-whether-yourecreditworthy-11560218700, June 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_056_w2aab3b7c30b1b6b1ab1ac56Aa","doi-asserted-by":"crossref","unstructured":"[56] J. Lin, S. Amini, J. I. Hong, N. Sadeh, J. Lindqvist, and J. Zhang. Expectation and purpose: Understanding users\u2019 mental models of mobile app privacy through crowdsourcing. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Ubicomp \u201912), pages 501\u2013510. ACM, 2012.10.1145\/2370216.2370290","DOI":"10.1145\/2370216.2370290"},{"key":"2022050115345152983_j_popets-2021-0028_ref_057_w2aab3b7c30b1b6b1ab1ac57Aa","unstructured":"[57] J. Lin, B. Liu, N. Sadeh, and J. I. Hong. Modeling users\u2019 mobile app privacy preferences: Restoring usability in a sea of permission settings. In Proceedings of the Tenth Symposium on Usable Privacy and Security (SOUPS \u201914), pages 199\u2013212, 2014."},{"key":"2022050115345152983_j_popets-2021-0028_ref_058_w2aab3b7c30b1b6b1ab1ac58Aa","unstructured":"[58] B. Liu, M. S. Andersen, F. Schaub, H. Almuhimedi, S. A. Zhang, N. Sadeh, Y. Agarwal, and A. Acquisti. Follow my recommendations: A personalized privacy assistant for mobile app permissions. In Twelfth Symposium on Usable Privacy and Security (SOUPS \u201916), pages 27\u201341, 2016."},{"key":"2022050115345152983_j_popets-2021-0028_ref_059_w2aab3b7c30b1b6b1ab1ac59Aa","doi-asserted-by":"crossref","unstructured":"[59] B. Liu, J. Lin, and N. Sadeh. Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help? In Proceedings of the 23rd International Conference on World Wide Web (WWW \u201914), pages 201\u2013212, New York, NY, USA, 2014.10.1145\/2566486.2568035","DOI":"10.1145\/2566486.2568035"},{"key":"2022050115345152983_j_popets-2021-0028_ref_060_w2aab3b7c30b1b6b1ab1ac60Aa","unstructured":"[60] B. Logan. Pay-per-laugh: the comedy club that charges punters having fun. https:\/\/www.theguardian.com\/stage\/2014\/oct\/14\/standup-comedy-pay-per-laugh-charge-barcelona, October 2014. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_061_w2aab3b7c30b1b6b1ab1ac61Aa","doi-asserted-by":"crossref","unstructured":"[61] N. K. Malhotra, S. S. Kim, and J. Agarwal. Internet users\u2019 information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4):336\u2013355, 2004.","DOI":"10.1287\/isre.1040.0032"},{"key":"2022050115345152983_j_popets-2021-0028_ref_062_w2aab3b7c30b1b6b1ab1ac62Aa","doi-asserted-by":"crossref","unstructured":"[62] L. Y. Mano et al. Exploiting IoT technologies for enhancing health smart homes through patient identification and emotion recognition. Computer Communications, 89-90:178\u2013190, 2016.10.1016\/j.comcom.2016.03.010","DOI":"10.1016\/j.comcom.2016.03.010"},{"key":"2022050115345152983_j_popets-2021-0028_ref_063_w2aab3b7c30b1b6b1ab1ac63Aa","doi-asserted-by":"crossref","unstructured":"[63] K. Martin and K. Shilton. Putting mobile application privacy in context: An empirical study of user privacy expectations for mobile devices. The Information Society, 32(3):200\u2013216, 2016.","DOI":"10.1080\/01972243.2016.1153012"},{"key":"2022050115345152983_j_popets-2021-0028_ref_064_w2aab3b7c30b1b6b1ab1ac64Aa","unstructured":"[64] D. Murph. SceneTap app analyzes pubs and clubs in real-time, probably won\u2019t score you a Jersey Shore cameo. https:\/\/www.engadget.com\/2011\/06\/12\/scenetap-app-analyzes-pubs-and-clubs-in-real-time-probably-won\/, June 2011. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_065_w2aab3b7c30b1b6b1ab1ac65Aa","unstructured":"[65] P. E. Naeini, S. Bhagavatula, H. Habib, M. Degeling, L. Bauer, L. F. Cranor, and N. Sadeh. Privacy expectations and preferences in an iot world. In Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS \u201917), pages 399\u2013412, 2017."},{"key":"2022050115345152983_j_popets-2021-0028_ref_066_w2aab3b7c30b1b6b1ab1ac66Aa","unstructured":"[66] NEC Corporation. New biometric identification tools used in theme parks. https:\/\/www.nec.com\/en\/global\/about\/mitatv\/03\/3.html, 2002. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_067_w2aab3b7c30b1b6b1ab1ac67Aa","unstructured":"[67] H. Nissenbaum. Privacy as contextual integrity. Washington Law Review, 79(1):119, 2004."},{"key":"2022050115345152983_j_popets-2021-0028_ref_068_w2aab3b7c30b1b6b1ab1ac68Aa","doi-asserted-by":"crossref","unstructured":"[68] H. Nissenbaum. Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press, 2009.10.1515\/9780804772891","DOI":"10.1515\/9780804772891"},{"key":"2022050115345152983_j_popets-2021-0028_ref_069_w2aab3b7c30b1b6b1ab1ac69Aa","unstructured":"[69] PCMag Stuff. NEC unveils facial-recognition system to identify shoppers. https:\/\/www.pcmag.com\/archive\/nec-unveils-facial-recognition-system-to-identify-shoppers-305015, November 2012. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_070_w2aab3b7c30b1b6b1ab1ac70Aa","doi-asserted-by":"crossref","unstructured":"[70] V. Pejovic, N. Lathia, C. Mascolo, and M. Musolesi. Mobile-Based Experience Sampling for Behaviour Research, pages 141\u2013161. Springer International Publishing, 2016.10.1007\/978-3-319-31413-6_8","DOI":"10.1007\/978-3-319-31413-6_8"},{"key":"2022050115345152983_j_popets-2021-0028_ref_071_w2aab3b7c30b1b6b1ab1ac71Aa","doi-asserted-by":"crossref","unstructured":"[71] C. Perera, R. Ranjan, L. Wang, S. U. Khan, and A. Y. Zomaya. Big data privacy in the internet of things era. IT Professional, 17(3):32\u201339, 2015.10.1109\/MITP.2015.34","DOI":"10.1109\/MITP.2015.34"},{"key":"2022050115345152983_j_popets-2021-0028_ref_072_w2aab3b7c30b1b6b1ab1ac72Aa","doi-asserted-by":"crossref","unstructured":"[72] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials, 16(1):414\u2013454, 2013.","DOI":"10.1109\/SURV.2013.042313.00197"},{"key":"2022050115345152983_j_popets-2021-0028_ref_073_w2aab3b7c30b1b6b1ab1ac73Aa","doi-asserted-by":"crossref","unstructured":"[73] P. Porambage, M. Ylianttila, C. Schmitt, P. Kumar, A. Gurtov, and A. V. Vasilakos. The quest for privacy in the internet of things. IEEE Cloud Computing, 3(2):36\u201345, 2016.10.1109\/MCC.2016.28","DOI":"10.1109\/MCC.2016.28"},{"key":"2022050115345152983_j_popets-2021-0028_ref_074_w2aab3b7c30b1b6b1ab1ac74Aa","unstructured":"[74] J. Porter. Federal study of top facial recognition algorithms finds \u2018empirical evidence\u2019 of bias. https:\/\/www.theverge.com\/2019\/12\/20\/21031255\/facial-recognition-algorithm-bias-gender-race-age-federal-nest-investigation-analysis-amazon, December 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_075_w2aab3b7c30b1b6b1ab1ac75Aa","doi-asserted-by":"crossref","unstructured":"[75] S. Prabhakar, S. Pankanti, and A. K. Jain. Biometric recognition: Security and privacy concerns. IEEE Security & Privacy, 1(2):33\u201342, March 2003.10.1109\/MSECP.2003.1193209","DOI":"10.1109\/MSECP.2003.1193209"},{"key":"2022050115345152983_j_popets-2021-0028_ref_076_w2aab3b7c30b1b6b1ab1ac76Aa","unstructured":"[76] Press Association. Tesco\u2019s plan to tailor adverts via facial recognition stokes privacy fears. https:\/\/www.theguardian.com\/business\/2013\/nov\/03\/privacy-tesco-scan-customers-faces, November 2013. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_077_w2aab3b7c30b1b6b1ab1ac77Aa","unstructured":"[77] E. Rader. Most Americans don\u2019t realize what companies can predict from their data. https:\/\/bigthink.com\/technology-innovation\/most-americans-dont-realize-what-companies-can-predict-from-their-data-2629911919, February 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_078_w2aab3b7c30b1b6b1ab1ac78Aa","unstructured":"[78] E. Ramirez, J. Brill, M. K. Ohlhausen, J. D. Wright, and T. McSweeny. Data brokers: A call for transparency and accountability. Technical report, Federal Trade Commission, May 2014."},{"key":"2022050115345152983_j_popets-2021-0028_ref_079_w2aab3b7c30b1b6b1ab1ac79Aa","doi-asserted-by":"crossref","unstructured":"[79] B. Rashidi, C. Fung, and T. Vu. Dude, ask the experts!: Android resource access permission recommendation with RecDroid. In 2015 IFIP\/IEEE International Symposium on Integrated Network Management (IM), pages 296\u2013304, 2015.10.1109\/INM.2015.7140304","DOI":"10.1109\/INM.2015.7140304"},{"key":"2022050115345152983_j_popets-2021-0028_ref_080_w2aab3b7c30b1b6b1ab1ac80Aa","doi-asserted-by":"crossref","unstructured":"[80] R. W. Reeder, A. P. Felt, S. Consolvo, N. Malkin, C. Thompson, and S. Egelman. An experience sampling study of user reactions to browser warnings in the field. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI \u201918), pages 1\u201313, 2018.10.1145\/3173574.3174086","DOI":"10.1145\/3173574.3174086"},{"key":"2022050115345152983_j_popets-2021-0028_ref_081_w2aab3b7c30b1b6b1ab1ac81Aa","unstructured":"[81] T. Revell. Computer vision algorithms pick out petty crime in CCTV footage. https:\/\/www.newscientist.com\/article\/2116970-computer-vision-algorithms-pick-out-petty-crime-in-cctv-footage\/, January 2017. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_082_w2aab3b7c30b1b6b1ab1ac82Aa","unstructured":"[82] D. Rosen. Disney is spying on you! https:\/\/www.salon.com\/test\/2013\/01\/17\/disney_is_spying_on_you\/, January 2013. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_083_w2aab3b7c30b1b6b1ab1ac83Aa","unstructured":"[83] T. S. Saponas, J. Lester, C. Hartung, S. Agarwal, and T. Kohno. Devices that tell on you: Privacy trends in consumer ubiquitous computing. In 16th USENIX Security Symposium (USENIX Security \u201907), pages 55\u201370, 2007."},{"key":"2022050115345152983_j_popets-2021-0028_ref_084_w2aab3b7c30b1b6b1ab1ac84Aa","unstructured":"[84] F. Schaub, R. Balebako, A. L. Durity, and L. F. Cranor. A design space for effective privacy notices. In Proceedings of the Eleventh Symposium On Usable Privacy and Security (SOUPS \u201915), pages 1\u201317, 2015."},{"key":"2022050115345152983_j_popets-2021-0028_ref_085_w2aab3b7c30b1b6b1ab1ac85Aa","unstructured":"[85] E. J. Schultz. Facial-recognition lets marketers gauge consumers\u2019 real responses to ads. https:\/\/adage.com\/article\/digital\/facial-recognition-lets-marketers-gauge-real-responses\/298635, May 2015. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_086_w2aab3b7c30b1b6b1ab1ac86Aa","unstructured":"[86] S. Shan, E. Wenger, J. Zhang, H. Li, H. Zheng, and B. Y. Zhao. Fawkes: Protecting privacy against unauthorized deep learning models. In 29th USENIX Security Symposium (USENIX Security \u201920), pages 1589\u20131604, August 2020."},{"key":"2022050115345152983_j_popets-2021-0028_ref_087_w2aab3b7c30b1b6b1ab1ac87Aa","doi-asserted-by":"crossref","unstructured":"[87] M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS \u201916), pages 1528\u20131540, 2016.10.1145\/2976749.2978392","DOI":"10.1145\/2976749.2978392"},{"key":"2022050115345152983_j_popets-2021-0028_ref_088_w2aab3b7c30b1b6b1ab1ac88Aa","doi-asserted-by":"crossref","unstructured":"[88] F. Shih, I. Liccardi, and D. Weitzner. Privacy tipping points in smartphones privacy preferences. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI \u201915), pages 807\u2013816, 2015.10.1145\/2702123.2702404","DOI":"10.1145\/2702123.2702404"},{"key":"2022050115345152983_j_popets-2021-0028_ref_089_w2aab3b7c30b1b6b1ab1ac89Aa","unstructured":"[89] E. Silverstein. New Konami casino facial recognition technology could rival reward cards. https:\/\/www.casino.org\/news\/new-konami-casino-facial-recognition-technology-could-rival-reward-cards\/, October 2019. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_090_w2aab3b7c30b1b6b1ab1ac90Aa","unstructured":"[90] A. Smith. More than half of U.S. adults trust law enforcement to use facial recognition responsibly. Technical report, Pew Research Center, September 2019."},{"key":"2022050115345152983_j_popets-2021-0028_ref_091_w2aab3b7c30b1b6b1ab1ac91Aa","doi-asserted-by":"crossref","unstructured":"[91] D. Smullen, Y. Feng, S. Zhang, and N. M. Sadeh. 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\u2013215, 2020.","DOI":"10.2478\/popets-2020-0011"},{"key":"2022050115345152983_j_popets-2021-0028_ref_092_w2aab3b7c30b1b6b1ab1ac92Aa","unstructured":"[92] B. Snyder. This beer ad only works when women pass by. https:\/\/fortune.com\/2015\/05\/21\/astra-beer-ad\/, May 2015. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_093_w2aab3b7c30b1b6b1ab1ac93Aa","unstructured":"[93] U.S. Government Accountability Office. Face recognition technology: FBI should better ensure privacy and accuracy. https:\/\/www.gao.gov\/assets\/680\/677098.pdf, May 2016. Accessed: 2019-11-22."},{"key":"2022050115345152983_j_popets-2021-0028_ref_094_w2aab3b7c30b1b6b1ab1ac94Aa","doi-asserted-by":"crossref","unstructured":"[94] N. Van Berkel, D. Ferreira, and V. Kostakos. The experience sampling method on mobile devices. ACM Computing Surveys (CSUR), 50(6):1\u201340, 2017.10.1145\/3123988","DOI":"10.1145\/3123988"},{"key":"2022050115345152983_j_popets-2021-0028_ref_095_w2aab3b7c30b1b6b1ab1ac95Aa","doi-asserted-by":"crossref","unstructured":"[95] S. J. Verhagen, L. Hasmi, M. Drukker, J. van Os, and P. A. Delespaul. Use of the experience sampling method in the context of clinical trials. Evidence-based Mental Health, 19(3):86\u201389, 2016.10.1136\/ebmental-2016-102418504076227443678","DOI":"10.1136\/ebmental-2016-102418"},{"key":"2022050115345152983_j_popets-2021-0028_ref_096_w2aab3b7c30b1b6b1ab1ac96Aa","unstructured":"[96] J. Whitely. How facial recognition technology is being used, from police to a soccer museum. https:\/\/www.wfaa.com\/article\/features\/originals\/how-facial-recognition-technology-is-being-used-from-police-to-a-soccer-museum\/287-618278039, November 2018. Accessed: 2020-12-15."},{"key":"2022050115345152983_j_popets-2021-0028_ref_097_w2aab3b7c30b1b6b1ab1ac97Aa","doi-asserted-by":"crossref","unstructured":"[97] P. Wijesekera, A. Baokar, L. Tsai, J. Reardon, S. Egelman, D. Wagner, and K. Beznosov. The feasibility of dynamically granted permissions: Aligning mobile privacy with user preferences. In 2017 IEEE Symposium on Security and Privacy, pages 1077\u20131093, 2017.10.1109\/SP.2017.51","DOI":"10.1109\/SP.2017.51"},{"key":"2022050115345152983_j_popets-2021-0028_ref_098_w2aab3b7c30b1b6b1ab1ac98Aa","doi-asserted-by":"crossref","unstructured":"[98] P. Wijesekera, J. Reardon, I. Reyes, L. Tsai, J.-W. Chen, N. Good, D. Wagner, K. Beznosov, and S. Egelman. Contextualizing privacy decisions for better prediction (and protection). In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI \u201918), pages 1\u201313, 2018.10.1145\/3173574.3173842","DOI":"10.1145\/3173574.3173842"},{"key":"2022050115345152983_j_popets-2021-0028_ref_099_w2aab3b7c30b1b6b1ab1ac99Aa","doi-asserted-by":"crossref","unstructured":"[99] Z. Wu, S.-N. Lim, L. S. Davis, and T. Goldstein. Making an invisibility cloak: Real world adversarial attacks on object detectors. In A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, editors, Computer Vision \u2013 ECCV 2020, pages 1\u201317. Springer International Publishing, 2020.10.1007\/978-3-030-58548-8_1","DOI":"10.1007\/978-3-030-58548-8_1"},{"key":"2022050115345152983_j_popets-2021-0028_ref_100_w2aab3b7c30b1b6b1ab1ad100Aa","unstructured":"[100] S. A. Zhang, Y. Feng, A. Das, L. Bauer, L. Cranor, and N. Sadeh. Understanding people\u2019s privacy attitudes towards video analytics technologies. Technical Report CMU-ISR-20-114, Carnegie Mellon University, School of Computer Science, December 2020."}],"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/popets-2021-0028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T16:31:26Z","timestamp":1658334686000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2021\/popets-2021-0028.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":100,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1,29]]},"published-print":{"date-parts":[[2021,4,1]]}},"alternative-id":["10.2478\/popets-2021-0028"],"URL":"https:\/\/doi.org\/10.2478\/popets-2021-0028","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}