ABSTRACT
Communications metadata can be used to determine a communication’s device, identify the user of the device, and profile the user’s personality and behavior. The current state of affairs is that the increase of attacks against user privacy based on using communications metadata vastly outpaces the ability of users to protect themselves. With few exceptions, protections are point solutions against a specific attack. In the current situation, the user loses.
This paper is an initial step in a multi-step research effort to reset that balance. The main contribution of this paper is a categorization of the uses of communications metadata based on their privacy impact. Because of the technical complexity of the problem, including the wide variety of electronic communications, technology can only go so far in providing solutions to the privacy problems created by the use of communications metadata. Legal and policy intervention will also be needed. This categorization is intended to provide a start in developing legal and policy privacy protections for communications metadata. Along the way, I also provide an explanation for how it is that communications metadata has become so valuable, sometimes surpassing the value of content. This work provides both an intellectual framework for thinking about the privacy implications of the use of communications metadata and a roadmap, with first steps taken, for providing privacy protections for users of electronic communications.
- Acar, Gunes, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, Claudia Diaz, “The Web Never Forgets: Persistent Tracking in the Wild,” Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, November 2014, pp. 674–689.Google Scholar
- Amar, Yousef, Hamed Haddadi, Richard Mortier, Anthony Brown, James Colley, and Andy Crabtree, “‘An analysis of home iot network traffic and behaviour,” https://arxiv.org/abs/1803.05368.Google Scholar
- Amerini, Irene, Rudy Becarelli, Roberto Caldelli, Alessio Melani, and Moreno Niccolai, “Combining Features of On-Board Sensors,” IEEE Transactions of Information Forensics and Security, Vol. 12, No. 10 (October 20, 2017).Google Scholar
- Anderson, Nate, “How ’cell tower dumps’ caught the High Country Bandits—and why it matters,” ArsTechnica, August 29, 2013 [last viewed May 21, 2020].Google Scholar
- Android, Android 9 Release Notes, https://source.android.com/setup/start/p-release-notes [last viewed May 17, 2020].Google Scholar
- Apple and Google, “Exposure Notification FAQ 1.1,” https://blog.google/documents/73/Exposure_Notification_-_FAQ_v1.1.pdf[last viewed May 21 2020].Google Scholar
- Apthorpe, Noah, Dillon Reisman, Nick Feamster, “A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic,” May 18, 2017.Google Scholar
- Apthorpe, Noah, Danny Yuxing Huang, Dillon Reisman, Arvind Narayanan, and Nick Feamster, “Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping, Proceedings on Privacy Enhancing Technologies, 2019, pp. 128–148.Google Scholar
- Alaca, Furkan and Paul van Oorschot, “Device Fingerprinting for Augmenting Web Authentication:Classification and Analysis of Methods,” Annual Computer Security Applications Conference (ASAC’32), 2016.Google Scholar
- Arackaparambil, Chrisil, Sergey Bratus, Anna Shubina, and David Kotz, “On the Reliability of Wireless Fingerprinting using Clock Skews,” Proceedings ACM WiSec,2010.Google Scholar
- Backes, Michael, Goran Doychev, Markus Durmuth, and Boris Kopf, “Speaker Recognition in Encrypted Voice Streams,” European Symposium on Research in Computer Security, 2010, pp. 508-523.Google Scholar
- Bakeless, John, Spies of the Confederacy,J.P. Lippincott, 1970.Google Scholar
- Bajardi, Paolo, Matteo Delfino, Andre Panisson, Giovanni Petri, and Michele Tizzoni, “Unveiling patterns of international communities in a global city using mobile phone data,”EPJ Data Science, Vol. 4, Article 3 (2015).Google ScholarCross Ref
- BBC News, “Yo app warns Israeli citizens of missile strikes,” July 14, 2014.Google Scholar
- Becker. R., R. Cáceres, K. Hanson, J. Loh, S. Urbanek, A. Varshavsky, and C. Volinsky. “A Tale of One City: Using Cellular Network Data for Urban Planning,” IEEE Pervasive Computing, Vol. 10, No. 4 (October-December 2011).Google Scholar
- Beddit Sleep Monitor, http://www.beddit.com/Google Scholar
- Bellovin, Steven, Matt Blaze, Susan Landau, and Stephanie Pell, “It’s Too Complicated: How the Internet Upends Katz, Smith, and Electronic Surveillance Law,” Harvard Journal of Law and Technology, Vol. 30, No. 1 (2017).Google Scholar
- Bengtsson Linus, Jean Gaudart, Xin Lu, Sandra Moore, Erik Wetter, Kankoe Sallah, Stanislas Rebaudet, and Renaud Piarroux, “Using Mobile Phone Data to Predict the Spatial Spread of Cholera,” Scientific Reports, Vol. 5, Article 8923 (2015).Google ScholarCross Ref
- Bengtsson, Linus, Xin Lu, Anna Thorson, Richard Garfield, and Johan von Schreeb, “Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti,” PLOS Medicine, August 30, 2011.Google Scholar
- Bensinger, Greg, “Talking Less, Paying More for Voice,” Wall Street Journal, June 6, 2012.Google Scholar
- Beresford Alaister, Stajano Frank, “Location privacy in pervasive computing,” Pervasive Computing, January-March 2003.Google Scholar
- Bergman, Ronen, “The Hezbollah Connection,” New York Times Magazine, February 10, 2015.Google Scholar
- Blondel, Vincent and Gautier Krings, NetMob 2011: Book of Abstracts,, 2011.Google Scholar
- Blondel, Vincent, Adeline Decuyper, Pierre Deville, Yves-Alexandre De Montjoye, Jameson Toole, Vincent Traag, and Dashun Wang, eds., Mobile Phone Data for Development: Analysis of Mobile Phone Datasets for the Development of the Ivory Coast; Selected Contributions to the D4D Challenge Sponsored by Orange, May 1-3, 2013, https://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf.Google Scholar
- Blondel, Vincent, Adeline Decuyper, and Guatier Krings, “A survey on results of mobile phone analysis,” EPJ Data Science, Vol. 4, No. 10 (2015).Google Scholar
- Blumenstock, Joshua, Gabriel Cadamuro, and Robert On, “Predicting poverty and wealth from mobile phone metadata,” Science, Vol. 350, Issue 6264 (November 27, 2015), pp. 1073-1076.Google ScholarCross Ref
- Bojinov, Hristo, Yan Michalevsky, Gabi Nakibly, and Dan Boneh, “Mobile Device Identification via Sensor Fingerprinting,” 2014.Google Scholar
- Borgman, Christine, Jillian Wallis, and Matthew Mayernick, “Who’s Got the Data? Interdependencies in Science and Technology Collaborations,” Computer Supported Cooperative Work, Vol. 21, Issue 6 (2012), pp. 485-523.Google ScholarCross Ref
- Borrmann, Donald A., William T. Kvetkas, Charles V. Brown, Michael J. Flatley, and Robert Hunt, The History of Traffic Analysis: World War I—Vietnam, Center for Cryptologic History, National Security Agency, 2013.Google Scholar
- Borgman, Christine, Big data, little data, no data: Scholarship in the networked world, MIT Press, 2015.Google Scholar
- Brandeis, Louis. Dissenting opinion in Olmstead v. United States, 277 U.S. 438, 1928.Google Scholar
- “The Essence of the Fundamental Rights to Privacy and Data Protection: Finding the Way Through the Maze ofthe CJEU’s Constitutional Reasoning,” German Law Journal, Vol. 20, pp. 864-883, 2019.Google ScholarCross Ref
- Brunson, Jason Cory and Richard C. Laubenbacher, “Applications of Network Analysis to Routinely Collected Health Care Data: A Systematic Review,” JAMIA, Vol. 25, Issue 2 (2018), pp. 210-221.Google Scholar
- Calabrese, Francesco, Laura Ferrari, and Vincent D. Blondel, “Urban Sensing Using Mobile Phone Network Data: A Survey of Research,” ACM Computing Surveys, Vol. 47, No. 2, Article 25 (November 2014).Google Scholar
- Calabrese, Francesco, Piero Lovisolo, Colonna Massimo, Dario Parata, and Carlo Ratti, “Real-Time Urban Monitoring Using CellPhones: A Case Study in Rome,” Transactions on IntelligentTransportation Systems, Vol. 12, No. 1 (March 2011), pp. 141-151.Google Scholar
- Calabrese, Francesco, Estaban Moro, Vincent Blondel, and Alex ’Sandy’ Pentland, eds., NetMob: Book of Abstracts, 2017, https://netmob.org/www17/assets/img/bookofabstract_oralt_2017.pdf.Google Scholar
- Carpita, Maurizio and Anna Simonetto, “Big Data to Monitor Big Social Events: Analysing the mobile phone signals inthe Brescia Smart City,” Electronic Journal of Applied Statistical Analysis, Vol. 05, Issue 01, December 2014, pp. 31-41.Google Scholar
- Chairunnunda, Prima, Nam Pham, and Urs Hengartner, “Privacy: Gone with the Typing! Identifying Web Users by their Typing Patterns,” PETS 2011.Google Scholar
- Chen, Ben, “Systems and Methods for Utilizing Wireless Communications to Suggest Connections for a User,” United States Patent Application 20160014677, July 10, 2014, http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=1&p=1&f=G&l=50&d=PG01&S1=%2820160114.PD.+AND+%28Facebook.AS.+OR+Facebook.AANM.%29%29&OS=PD/1/14/2016+and+%28AN/Facebook+or+AANM/Facebook%29&RS=%28PD/Google Scholar
- Chen, M. Keith and Ryne Rohla, “The effect of partisanship and political advertising on close family ties,” Science, Vol. 360, Issue 6392, pp. 1020-1024.Google Scholar
- Chen, You, Nancy Lorenzi, Steve Nyemba, Jonathan Schildcrout, and Bradley Malin, “We Work with Them? Healthcare Workers Interpretation of Organizational Relations Mined from Electronic Health Records,” International Journal of Medical Information, Vol. 83, No. 7 (2014).Google Scholar
- Chen, You, Nancy Lorenzi, Warren Sandberg, Kelly Walgast, and Bradley Malin, “Identifying Collaborative Care Teams through Electronic Medical Utilization Records,” Journal of the American Medical Informatics Association, Volume 24, Issue e1, April 2017, Pages e111–e120,Google Scholar
- Chu, Zi, Steven Gianvecchio, Haining Wang, and Sushil Jajodia, “Who is Tweeting on Twitter: Human, Bot, or Cyborg?,” ACSAC, 2010, https://www.eecis.udel.edu/ hnw/paper/acsac10.pdf.Google Scholar
- Cisco, NetFlow Configuration Guide, Cisco IOS Release 15M& T,https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/netflow/configuration/15-mt/nf-15-mt-book/ios-netflow-ov.html#GUID-0C91B715-F791-4F90-BF13-4654A1D7AFBB [last viewed November 10, 2020].Google Scholar
- Clark, David, “The Design Philosophy of the DARPA Internet Protocols,” Proceedings SIGCOMM 88, Computer Communication Review, Vol. 18, No. 4, August 1988, pp. 106-114.Google Scholar
- Cole, Matthew, “OPSEC Failure of Spies,” Black Hat USA 2013, December 3, 2013, https://www.youtube.com/watch?v=BwGsr3SzCZc.Google Scholar
- Cole, David, “We Kill People Based on Metadata,” New York Review of Books, May 10, 2014.Google Scholar
- Conover, Michael, Clayton Davis, Emilio Ferrara, Karissa McKelvery, Filippo Menczer, and Alessandro Flammini, “The Geospatial Characteristics of a Social Movement Communication Network,” PLOS ONE, March 6, 2013.Google Scholar
- Committee on Responding to Section 5(d) of Presidential Policy Directive 28: The Feasibility of Software to Provide Alternatives to Bulk Signals Intelligence Collections, National Research Council, Bulk Collection of Signals Intelligence: Technical Options, National Academies Press, 2015.Google Scholar
- Cortes, Corrina, Daryl Pregibon, and Chris Volinsky, “Communities of Interest,” IDA ’01: Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis, September 2001, pp. 105-114.Google Scholar
- Das, Anupam, Nikita Borisov, and Edward Chou, “Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and Countermeasures,” Proceedings in Privacy Enhancing Technologies, 2018 (1), pp. 88-108.Google Scholar
- Daubert, Jorg, Alexander Wiesmaier, and Panayotis Kikiras, “A View on Privacy & Trust in IoT,” IEEE ICC—Workshop on Security and Privacy for Internet of Things and Cyber-Physical Systems, 2015.Google Scholar
- Deville, Pierre, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R. Stevens, Andrea E. Gaughan, Vincent D. Blondel, and Andrew J. Tatem, “Dynamic population mapping using mobile phone data,” Proceedings of the National Academy of Science, 111(45) (2014), pp. 15888-15893.Google ScholarCross Ref
- Dey, Sanorita, Nirupam Roy, Wenyuan Xu, Romit Roy Choudhury, and Srihari Nelakuditi, “AccelPrint: Imperfections of Accelerometers MakeSmartphones Trackable,” NDSS 2014.Google Scholar
- Diesner, J. and K.M. Carley, “Exploration of Communication Networks from the Enron Email Corpus,” Workshop on Link Analysis, Counterterrorism, and Security, April 23, 2005.Google Scholar
- Diesner, Jana, Terrill L. Frantz, and Kathleen M. Carley, “Communication Networks from the Enron Email Corpus: ’It’s Always About the People. Enron is no Different’,” Computational and Mathematical Organization Theory, Vol. 11 (2005), pp. 201-228.Google ScholarDigital Library
- Dobra, Adrian, Nathelie E. Williams, and Nathan Eagle, “Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data,” PLOS One, Vol. 10, NO.3 (2015).Google Scholar
- Douglass, Rex W., David A. Meyer, Megha Ram, David Rideout, and Dongjin Song, “High resolution population estimates from telecommunications data,” EPJ Data Science, Vol. 4, Article 4 (2015).Google ScholarCross Ref
- Dublin Core Metadata Initiative, “DCMI Type Vocabulary,” http://dublincore.org/documents/dcmi-terms/#section-7 [last viewed December 22, 2017].Google Scholar
- Eagle, Nathan, Michael Macy, and Rob Claxton, “Network Diversity and Economic Development,” Science,Vol. 328 (May 21, 2010), pp. 1029-1031.Google ScholarCross Ref
- Eckersley, Peter, “How Unique is Your Web Browser,” Privacy Enhancing Technologies Symposium, 2010.Google Scholar
- European Commission, “Proposal for a Regulation of the European Parliament and of the Council concerning the respect for privacy life and the protection of personal data in electronic communications and repealing Directive 2002/58/EC (Regulation on Privacy and Electronic Communications), 2017/0003 (COD), Brussels, October 1, 2017.Google Scholar
- European Commission, Directive 2009/136/EC of the European Parliament and the Council, November 25, 2009.Google Scholar
- Federal Chief Information Officers Council and Federal Enterprise Architecture, Federal Identity, Credential, and Access Management (FICAM) Roadmap and Implementation Guidance, Version 2.0, December 2, 2011.Google Scholar
- Filippova, Katja and Keith Hall, “Improved video categorization from text metadata and user comments,” SIGIR 2011.Google Scholar
- Fitzgerald, Patrick, “The Evolving Role of Technology in the Work of Leading Investigators and Prosecutors,” Palantir, June 12, 2013, accessed December 12, 2016, 11:39-12:44.Google Scholar
- Gambs, Sebastien, Marc-Olivier Killijian and Miguel Nunez del Prado Cortez, “De-anonymization attack on geolocated data,” Journal of Computer and System Sciences, Vol. 80, Issue 8 (December 2014), pp. 1597-1614.Google ScholarDigital Library
- Ginsburg, Douglas U.S. v. Maynard, 615 F. 3d 544 (D.C. Cir 2010).Google Scholar
- Gilliland, Anne, “Setting the Stage,” in Introduction to Metadata, second edition, Getty Research Institute, 2008.Google Scholar
- Golle, Philippe and Kurt Partridge, “On the Anonymity of Home/Work Location Pairs,” International Conference on Pervasive Computing, 2009, pp. 390-397.Google Scholar
- Google, “COVID-19 Community Mobility Report,” https://www.gstatic.com/covid19/mobility/2020-04-05_US_Mobility_Report_en.pdfGoogle Scholar
- Gorman, Siobhan, “NSA’s Domestic Spying Grows as Agency Sweeps Up Data,” Wall Street Journal, March 10, 2008.Google Scholar
- Gratzer, Vanessa and David Naccache, “Cryptog- raphy, Law Enforcement, and Mobile Communications,” IEEE Security and Privacy, Vol. 4, No. 6 (November/December 2006).Google Scholar
- Greenwald, Glenn, “NSA collecting phone records of millions of Americans daily,” Guardian, Jun 6, 2013.Google Scholar
- Greschbach, Benjamin, Gunnar Kreitz and Sonja Buchegger, “The Devil is in the Metadata—New Privacy Challenges in Decentralised Online Social Networks,” IEEE International Conference on Pervasive Computing and Communications Workshops, 2012.Google Scholar
- Griffiths, Rudyard, ed., Does State Spying Make Us Safer: the Munk Debate on Mass Surveillance 2014.Google Scholar
- Gundlegard, David, Clas Rydergren, Nils Bryeer, “Travel demand estimation and network assignment based on cellular network data,” Computer Communications (2016), pp. 29-42.Google Scholar
- Gungdogdu, Didem, Ozlem D. Incel, Albert A. Saleh, and Bruno Lepri, “Countrywide arrhythmia: emergency event detection using mobile phone data,” EPJ Data Science, Vol. 5, Article number 25 (2016).Google ScholarCross Ref
- Han, Shin-Kap, “The Other Ride of Paul Revere: The Brokerage Role in the Making of the American Revolution,” Mobilization: An International Quarterly, Vol. 14, Issue 2 (2009), pp. 143-162.Google ScholarCross Ref
- Harris, Shane, “How the NSA Became a Killing Machine,” The Daily Beast, November 9, 2014 (update April 14, 2017), https://www.thedailybeast.com/how-the-nsa-became-a-killing-machine [last viewed March 25, 2020].Google Scholar
- Hayden, Michael in “Johns Hopkins Foreign Affairs Symposium Presents: The Price of Privacy: Re-Evaluating the NSA,” April 7, 2014, at 17.59.Google Scholar
- Hern, Alex, “Fitness tracking app Strava gives away location of secret US army bases,” Guardian, January 28, 2018.Google Scholar
- Hersh, Seymour, “The Intelligence Gap,” New Yorker, December 6, 1999.Google Scholar
- Hu, Yunhua, Hang Li, Yunbo Cao, Li Teng, Dmitriy Meyerzon, and Qinghua Zheng, “Automatic extraction of titles from general documents using machine learning,”Information Processing and Management, 42 (2006) 1276–1293.Google ScholarDigital Library
- Hupperich, Thomas, Henry Hosseini, and Thorsten Holz, “Leveraging Sensor Fingerprinting for Mobile Device Identification,” Detection of Intrusions and Malware & Vulnerability Assessment—DIMVA 2016, pp. 377-396.Google Scholar
- Hupperich, Thomas, Davide Maiorca, Marc Kuhrer, Thorsten Holz, and Giorgio Giacinto, “On the Robustness of Mobile Device Fingerprinting: Can Mobile Users Escape Modern Web-Tracking Mechanisms?,” Annual Computer Security Applications Conference, December 2015.Google Scholar
- Isaacman, Sibren, Vanessa Frias-Martinez, Lingzi Hong, Enrique Frias-Martinez, “Climate Change Induced Migrations from a Cell Phone Perspective,” NetMob, p. 46, 2017.Google Scholar
- Jahani, Eaman, Pål Roe Sundsø, Johannes Bjelland, Asif Iqbal, Alex Pentland, and Yves-Alexandre, de Montjoye, “Predicting Gender from Mobile Phone Metadata in NetMob 2015.Google Scholar
- Jourdan, Theo, Antoine Boutet, and Carole Frindel, “Toward Privacy in IoT Devices for Activity Recognition,” 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Nov 2018).Google Scholar
- Kahn, David, The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet,Scribner, 1996.Google Scholar
- Karagiannis, Thomas, Andre Broido, Michalis Faloutsos, and kc claffy, “Transport Layer Identification of P2P Traffic,” IMC 2004.Google Scholar
- Karagiannis, Thomas, Konstantina Papagiannaka, and Michalis Faloutsos, BLINC: Multilevel Traffic Classification in the Dark, SIGCOMM’05, August 22-26, 2005.Google Scholar
- M. Karnan, M. Akila, and N. Krishnaraj, “Biometric personal authentication using keystroke dynamics: A review,” Applied Soft Computing, Vol. 11, Issue 2, pp: 1565-1573.Google ScholarDigital Library
- Kastrenakes, Jacob, “FCC fines Verizon $ 1.35 miillion over ’supercookie’ tracking,” TheVerge, March 7, 2016, https://www.theverge.com/2016/3/7/11173010/verizon-supercookie-fine-1-3-million-fcc.Google Scholar
- Kerr, Orin, “Websurfing and the Wiretap Act,” Washington Post, June 4, 2015.Google Scholar
- Kerr, Orin, “Websurfing and the Wiretap Act, part 2: the Third Circuit’s ruling,” Washington Post, November 19, 2015.Google Scholar
- Kim, Hyunchul, kc claffy, Maria Fomenkov, Dhiman Barman, Michalis Faloutsos, KiYoung Lee, “Internet Traffic Classification Demystified: Myths, Caveats, and the Best Practices,” ACM CoNEXT 2008, December 10-12, 2008.Google Scholar
- Klausen, Jyette, Christopher Marks, and Tauhid Zamen, “Finding Online Extremists in Social Networks,” Operations Research, Vol. 66, Issue 4 (August 2018), pp. 957-976.Google ScholarDigital Library
- Kohno, Tadayoshi, Andre Broido, and K.C. Claffy, “Remote physical device fingerprinting,” IEEE Transactions on Dependable and Secure Computing, Vol. 2, No. 2 (2005).Google Scholar
- Krystosek, Paul, Nancy Ott, Geoffrey Sanders, and Timothy Shimeall, Network Traffic Analysis with SiLK: Analyst’s Handbook for SiLK Version 3.15.0 and Later, August 2020.Google Scholar
- Krikorian, Raffi, “Map of a Twitter Status Object,” April 18, 2010.Google Scholar
- Kung, Kevin S., Kael Greco, Stanislav Sobelevsky, and Carlo Ratti, “Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data,” PLOS One, Vol. 9, No. 6 (2014).Google Scholar
- Laperdrix, Pierre, Walter Rudametkin, and Benoit Baudry, “Beauty and the Beast: Diverting modern webbrowsers to build unique browser fingerprints,” 37th IEEE Symposium on Security and Privacy, May 2016.Google Scholar
- Renaud Lambiotte, Estaban Moro, Vincent Blondel, and Alex “Sandy” Pentland, NetMob: Book of Abstracts, 2019.Google Scholar
- Landau, Susan, Hubert Le Van Gong, and Robin Wilton, “Achieving Privacy in a Federated Identity Management System,” Financial Cryptography and Data Security, 2009.Google Scholar
- Landau, Susan, “Under the Radar: NSA’s Efforts to Secure Private-Sector Telecommunications Infrastructure,” Journal of National Security Law and Policy, Vol. 7, No.3 (2014), pp. 411-442.Google Scholar
- Landau, Susan, “Transactional information is remarkably revelatory,” Proceedings of the National Academy of Sciences, Vol. 113, No. 20 (May 17, 2016), pp. 5467-5469.Google ScholarCross Ref
- Landau, Susan Listening In: Cybersecurity in an Insecure Age, Yale University Press, 2017.Google Scholar
- Landau, Susan and Asaf Lubin, “Examining the Anomalies, Explaining the Value: Should the USA FREEDOM Act’s Metadata Program be Extended?,” to appear, Harvard National Security Journal.Google Scholar
- Layton, Edward, And I was There: Pearl Harbor and Midway, William Morrow and Co., 1985.Google Scholar
- Lee, Bartholomew, “Wireless–Its Evolution from Mysterious Wonder to Weapon of War, 1902 to 1912,” https://www.californiahistoricalradio.com/wp-content/uploads/2013/01/BartWirelessWar190205Lee.pdf [last viewed May 5, 2020].Google Scholar
- Leith, Douglas, “Web Browser Privacy: What Do Browsers SayWhen They Phone Home?,” SCSS Technical Report, https://www.scss.tcd.ie/Doug.Leith/pubs/browser_privacy.pdf [last viewed May 17, 2020].Google Scholar
- Li, Huaxin, Zheyu Xu, Haojin Zhu, Di Ma, Shuan Li, and Kai Xing, “Demographics Inference Through Wi-Fi Network Traffic Analysis,” IEEE INFOCOM, 2016.Google Scholar
- Lichtblau, Eric, “Police are Using Phone Tracking as a Routine Tool,” New York Times, March 31, 2012.Google Scholar
- Lisovich, Michael, Deidre K. Mulligan, and Stephen Wicker, “Inferring Personal Information from Demand-Response Systems,” IEEE Security and Privacy, Vol. 8, No. 1 (January 2010), 11-20.Google ScholarDigital Library
- Lu, Xin David J. Wrathall, Pal Roe Sundsoye, Md. Nadiruzzaman, Erik Wetter, Asif Iqbal, Taimur Qureshi, Andrew Tatem, Geoffrey Canright, Kenth Engø-Monsen, Linus Bengtsson, “Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh,” Global Environment Change, Vol. 38 (May 2016), pp. 1-7.Google ScholarCross Ref
- Manousakas, Dionysis, Cecilia Mascolo, Alastair R. Beresford, Dennis Chan, and Nikhil Sharma, “Quantifying Privacy Loss of Human Mobility Graph Topology,” Privacy Enhancing Technologies Symposium, 2018.Google Scholar
- Ma, Huina, Xin Shuai. Yong-Yuel Ahn, and Yohan Bohlen, “Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to Cote I’voire,” EPJ Data Science, Vol. 4, Article 15 (2015).Google ScholarCross Ref
- Malmi, Eric and Igmar Weber, “You Are What Apps You Use: Demographic Prediction Based on User’s Apps,” Proceedings of the Tenth International AAAI Conference on Web and Social Media, 2016.Google Scholar
- Masood, Rahat, Benjamin, Zi Hao Zhao, Hassan Jameel Asghar, and Mohamed Ali Kaafar, “Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking,” PETS 2018.Google Scholar
- Jonathan Mayer, Patrick Mutchler, and John C. Mitchell, “Evaluating the privacy properties of telephone metadata,” Proc Natl Acad Sci., Vol. 113, No. 20 (May 17, 2016), pp. 5536-5541.Google ScholarCross Ref
- Matthew Mayernick and Amelia Acker, “Tracing the Traces: The Critical Role of Metadata within Networked Communications,” Journal of the Association for Information Science and Technology, September 19, 2017.Google Scholar
- Microsoft, “Windows 10, version 1709 basic level Windows diagnostic events and fields,” December 12, 2018 https://docs.microsoft.com/en-us/windows/privacy/basic-level-windows-diagnostic-events-and-fields-1709 [last viewed December 23, 2018].Google Scholar
- de Montjoye, Yves-Alexandre, Jordi Quoidbach, Florent Robic, and Alex (Sandy) Pentland, “Predicting Personality Using Novel Mobile Phone-Based Metrics,” Social Computing, Behavioral-Cultural Modeling and Prediction 2013, pp. 48–55.Google Scholar
- de Montjoye, Yves-Alexandre, Cesar A. Hidalgo, Michel Verleysen, and Vincent D Blondel, “Unique in the crowd: The privacy bounds of human mobility,” Scientific reports, Vol. 3 2013.Google Scholar
- de Montjoye1, Yves-Alexandre, Erez Shmueli, Samuel S. Wang, Alex Sandy Pentland, “openPDS: Protecting the Privacy of Metadata through SafeAnswers,” PLOS One, Vol. 9, Issue 7 (2015).Google Scholar
- de Montjoye, Yves-Alexandre, L. Radaelli, V. K. Singh, and A. Pentland, “Unique in the Shopping Mall: On the Reidentifiability of Credit Card Metadata,” Science, Vol. 347, no. 6221, pp. 536-539.Google Scholar
- Moore, Tyler and Richard Clayton, “Discovering Phishing Mailboxes Using Email Metadata,” Seventh APWG eCrime Researchers Summit (eCrime), Las Croabas, PR, October 2012.Google Scholar
- Motahari, Sara, Ole Mengshoel, Phyllis Reuther, Sandeep Appala, Luca Zoia, and Jay Shah, “The Impact of Social Affinity on Phone CallingPatterns: Categorizing Social Ties from Call Data Records,” Proceedings of the 6th Workshopon Social Network Mining and Analysis, 2012.Google Scholar
- Muriello, Daniel, Stephen Heise, and Jie Chen, “Associating Users and Cameras in a Social Networking System,” United States Patent 9,485,923, November 1, 2016, http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/9485423 [last viewed December 22, 2018].Google Scholar
- National Research Council, Bulk Collection of Signals Intelligence: Technical Options, National Academies Press, 2015.Google Scholar
- National Security Agency, “Tor Stinks,” https://edwardsnowden.com/docs/doc/tor-stinks-presentation.pdf [last viewed May 21, 2020].Google Scholar
- President Barack Obama, Remarks, https://edition.cnn.com/2013/06/07/politics/nsa-data-mining, June 10, 2013.Google Scholar
- Onnela, Jukka-Pekka, Samuel Arbesman, Marta C. Gonzalez, Albert-Laszlo Barabas, Nicholas A. Christakis, “Geographic Constraints on Social Network Groups,” PLOS One(2011).Google Scholar
- Pai, Sameer, Marci Meingast, Tanya Roosta, Sergio Bermudez, Stephen B. Wicker, Deirdre K. Mulligan, Shankar Sastrym “Transactional Confidentiality in Sensor Networks,” IEEE Security and Privacy, Vol. 6, No. 4, pp. 28-35, Jul/Aug, 2008.Google ScholarDigital Library
- Private communication with the author.Google Scholar
- Court of Justice of the European Union, Judgement of the Court, Patrick Breyer v. Germany, October 19, 2016.Google Scholar
- Patel, Vishal, Rama Chellappa, Deepak Chama, Brandon Barbello, “Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges,” IEEE Signal Processing Magazine, Vol. 33, Issue 4, pp. 49-61, July 1, 2016.Google ScholarCross Ref
- Pattara-Atikom, Wasan and Ratchata Peachavanish, “Estimating road traffic congestion from cell dwell time using neural network,” Proceedings from telecommunications, 7th international conference on ITS (pp. 1–6).Google Scholar
- Peng, Wei and Tong Sun, “Method and system for identifying a key influencer in social media utilizing topic modeling and social diffusion analysis,” US8312056B1, granted November 11, 2013.Google Scholar
- Pomerantz, Jeffrey, Metadata, MIT Press, 2015.Google Scholar
- Rowe, Ryan, German Creamer, Shlomo Heshkop, and Salvatore Stolfo, “Automated Social Hierarchy Detection through Email Network Analysis,” Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining Social Network Analysis,pp. 109-117, 2007.Google Scholar
- Sapiezynski, Piotr , Arkadiusz Stopczynski, Radu Gatej, and Sune Lehmann, “Tracking Human Mobility Using WiFi Signals,” PLOS One, July 1, 2015.Google Scholar
- Science and Technology Directorate, Department of Homeland Security, Study on Mobile Device Security, April 2017.Google Scholar
- Sen, Subhabrata, Oliver Spatscheck, and Dongmei Wang, “Accurate, Scalable In Network Idnentification of P2P Traffic Using Application Signatures,” WWW2004, May 17-22, 2004.Google Scholar
- Seneviratne, Suranga, Aruna Seneviratne, Prasant Mohapatra, and Anirban Mahanti, “Your Installed Apps Reveal Your Gender and More!,” Mobile Computing and Communications Review, Vol. 18, No. 3 (July 2014).Google ScholarDigital Library
- Sense Sleep Monitor, https://hello.is.Google Scholar
- Soto, Victor, Vanessa Frias-Martinez, Jesus Virseda and Enrique Frias-Martinez, “Prediction of Socioeconomic Levels Using Cell Phone Records,” International Conference on User Modeling, Adaptation, and Personalization, 2011.Google Scholar
- Statista, “Smartphone penetration rate as share of the population in the United States from 2010 to 2021,” https://www.statista.com/statistics/201183/forecast-of-smartphone-penetration-in-the-us/ [last viewed May 19, 2020].Google Scholar
- Steenbruggen, John, Maria Teresa Borzacchiello, Peter Nijkamp, and Henk Scholten, “Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities,” GeoJournal, Vol. 78 (2013), pp. 223-243.Google ScholarCross Ref
- Sterbenz, James, “Intelligence in Future Broad Networks: Challenges and Opportunities in High-Speed Networks,” 2002 International Zurich Seminar on Broadband Communications, 2002.Google Scholar
- Staiano, Jacopo, Fabio Pianesi, Bruno Lepri, Nicu Sebe, Nadav Aharony, and Alex Pentland, “Friends Don’t Lie,” Proceedings ofthe 2012 ACM Conference on Ubiquitous Computing—UbiComp, 2012.Google Scholar
- Subbian, Karthik, ”Offline Trajectories,” https://patents.justia.com/patent/10149111, December 4, 2018 [last viewed August 3, 2020].Google Scholar
- Tatem, Andrew, Youliang Qui, David L. Smith, Oliver Sabot, Abdullah S. Ali, and Bruno Moonen, “The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents,” Malaria Journal, Vol. 8, Article number: 287 (2009).Google ScholarCross Ref
- Twitter, “About public and protected tweets,” https://help.twitter.com/en/safety-and-security/public-and-protected-tweets [last viewed April 28, 2020].Google Scholar
- U.S. Patent Application Publication No. 2012/0304206 (November 29, 2012)Google Scholar
- Vaccari, A., F. Dal Fiore, E. Beinat, A. Biderman, and C. Ratti, “Current Amsterdam: studying social dynamics through mobile phones network Data,” Imagining Amsterdam,Amsterdam, Netherlands, 19–21 November 2009.Google Scholar
- In re Application of the FBI for an Order Requiring the Production of Tangible Things from Verizon Business Network Services, Inc. on Behalf of MCI Communication Services, Inc., No. BR 13- 80 (FISC Apr. 25, 2013)).Google Scholar
- Viana, Aline Carneiro, Adriano Di Luzio, Katia Jaffrès-Runser, Alessandro Mei, Julinda Stefa, “Accurately Inferring Personality Traits from the Use of Mobile Technology,” 2018.Google Scholar
- Weslowski, Amy, Nathen Eagle, Andew J. Tatem, David L. Smith, Abdisalan M. Noor, Robert W. Snow, Carolyn O. Buckee, “Quantifying the impact of human mobility on malaria,” Science, Vol. 338 (2012) 267–270.Google ScholarCross Ref
- Wesolowski, Amy, Nathan Eagle, Abdisalan M. Noor, Robert W. Snow, and Caroline O. Buckee, “The impact of biases in mobile phone ownership on estimates of human mobility,” Journal of the Royal Society (2013).Google Scholar
- Wesolowski, Amy, Taimur Qureshi, Maciej F. Boni, Pal Roe Sundsøy, Michael A. Johansson, Syed Basit Rasheed, Kenth Engo-Monsen, and Caroline O. Buckee, “Impact of human mobility on the emergence of dengue epidemics in Pakistan,” Proceedings of the National Academies of Science, September 22, 2015.Google Scholar
- Weyuker, Elaine, Thomas Ostrand, and Robert Bell, “D too many cooks spoil the broth? Using the number of developers to enhance detect prediction models?,” Journal of Empirical Software Engineering, Vol. 13, Issue 5, 2008.Google Scholar
- Wilkinson, Gerard, Tom Bartindale, Tom Nappey, Michael Evans, Peter Wright, and Patrick Olivier, “Media of Things: Supporting the Production of Metadata Rich Media Through IoT Sensing,” CHI, 2018.Google Scholar
- Williams, Nathelie E., Timothy A. Thomas, Matthew Dunbar, Nathan Eagle, Adrian Dobra, “Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data,” PLOS One,Vol. 10, Issue 7 (2015).Google Scholar
- Wolff, Josephine, You’ll See this Message when it is Too Late: The Legal and Economic Aftermath of Cybersecurity Breaches, MIT Press, 2019.Google Scholar
- Mobile Cellular Subscriptions (per 100 people), WORLD BANK, https://data.worldbank.org/indicator/IT.CEL.SETS.P2?Google Scholar
- Charles V. Wright, Lucas Ballard, Fabian Monrose, and Gerald M. Masso, “Language Identification of Encrypted VoIP Traffic: Alejandra y Roberto or Alice and Bob?,” Sixteenth USENIX Security Symposium, August 2007.Google Scholar
- Ziegeldorf, Jan Henrik, Oscar Garcia Morchon, and Klaus Wehrle, “Privacy in the Internet of Things: Threats and Challenges,” Security and Communication Networks, June 10, 2013.Google Scholar
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