A Survey of Privacy Vulnerabilities of Mobile Device Sensors

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

[1]  Aythami Morales,et al.  SensitiveNets: Learning Agnostic Representations with Application to Face Images. , 2020, IEEE transactions on pattern analysis and machine intelligence.

[2]  Catuscia Palamidessi,et al.  Geo-indistinguishability: differential privacy for location-based systems , 2012, CCS.

[3]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[4]  Niclas Palmius,et al.  SleepAp: An automated obstructive sleep apnoea screening application for smartphones , 2013, Computing in Cardiology 2013.

[5]  TemplMatthias.  Statistical Disclosure Control for Microdata Using the R-Package sdcMicro , 2008 .

[6]  Devu Manikantan Shila,et al.  Side channel attack on smartphone sensors to infer gender of the user: poster abstract , 2019, SenSys.

[7]  Ge Lin,et al.  Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach , 2016, Trans. GIS.

[8]  R. Poovendran,et al.  CARAVAN: Providing Location Privacy for VANET , 2005 .

[9]  Vivek Kanhangad,et al.  Gender recognition in smartphones using touchscreen gestures , 2019, Pattern Recognit. Lett..

[10]  Maxim Raya,et al.  Mix-Zones for Location Privacy in Vehicular Networks , 2007 .

[11]  Hamed Haddadi,et al.  A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics , 2017, IEEE Internet of Things Journal.

[12]  Masud Ahmed,et al.  Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review , 2019, 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR).

[13]  Amitava Mukherjee,et al.  Pervasive Computing: A Paradigm for the 21st Century , 2003, Computer.

[14]  Halgurd S. Maghdid,et al.  Gait-based Gender Classification Using Smartphone Accelerometer Sensor , 2019, 2019 5th International Conference on Frontiers of Signal Processing (ICFSP).

[15]  Dan Suciu,et al.  The Boundary Between Privacy and Utility in Data Publishing , 2007, VLDB.

[16]  Charu C. Aggarwal,et al.  On k-Anonymity and the Curse of Dimensionality , 2005, VLDB.

[17]  Mustafa Ahmet Afacan,et al.  Reliability of smartphone measurements of vital parameters: A prospective study using a reference method. , 2019, The American journal of emergency medicine.

[18]  Sanjay Kumar Singh,et al.  Privacy preservation for soft biometrics based multimodal recognition system , 2016, Comput. Secur..

[19]  Alberto Machì,et al.  From proximity to accurate indoor localization for context awareness in mobile museum guides , 2016, MobileHCI Adjunct.

[20]  Samit Bhattacharya,et al.  Towards affective touch interaction: predicting mobile user emotion from finger strokes , 2015, Journal of Interaction Science.

[21]  Hui Xiong,et al.  Preserving privacy in gps traces via uncertainty-aware path cloaking , 2007, CCS '07.

[22]  Vincenzo Piuri,et al.  Biometric Privacy Protection: Guidelines and Technologies , 2011, ICETE.

[23]  Arun Ross,et al.  50 years of biometric research: Accomplishments, challenges, and opportunities , 2016, Pattern Recognit. Lett..

[24]  Muhammad Usman Ilyas,et al.  Activity recognition using smartphone sensors , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[25]  Sabrina De Capitani di Vimercati,et al.  Data Privacy: Definitions and Techniques , 2012, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[26]  Naser Damer,et al.  Learning privacy-enhancing face representations through feature disentanglement , 2020, 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020).

[27]  Reza Vahidnia,et al.  Wearables and the Internet of Things (IoT), Applications, Opportunities, and Challenges: A Survey , 2020, IEEE Access.

[28]  Rui Zhang,et al.  A Hybrid Approach to Privacy-Preserving Federated Learning , 2018, Informatik Spektrum.

[29]  Robert Weibel,et al.  Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth , 2018, EPJ Data Science.

[30]  Marimuthu Palaniswami,et al.  Privacy-preserving collaborative fuzzy clustering , 2018, Data Knowl. Eng..

[31]  Ernesto Damiani,et al.  Location Privacy Protection Through Obfuscation-Based Techniques , 2007, DBSec.

[32]  Anil K. Jain,et al.  Biometric Template Protection: Bridging the performance gap between theory and practice , 2015, IEEE Signal Processing Magazine.

[33]  Nasir D. Memon,et al.  Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices , 2018, Comput. Secur..

[34]  M. Egger,et al.  Body mass index in midlife and dementia: Systematic review and meta-regression analysis of 589,649 men and women followed in longitudinal studies , 2017, Alzheimer's & dementia.

[35]  Z. Shinar,et al.  Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[36]  Fangyuan Zhao,et al.  Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning , 2018, JAMA network open.

[37]  Gregory D. Abowd,et al.  A practical approach for recognizing eating moments with wrist-mounted inertial sensing , 2015, UbiComp.

[38]  Maarten De Vos,et al.  Detecting Bipolar Depression From Geographic Location Data , 2016, IEEE Transactions on Biomedical Engineering.

[39]  Antoine Boutet,et al.  DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks , 2020, AsiaCCS.

[40]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[41]  Kato Mivule,et al.  Utilizing Noise Addition for Data Privacy, an Overview , 2013, ArXiv.

[42]  J. Dobner,et al.  Body mass index and the risk of infection - from underweight to obesity. , 2018, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[43]  Rama Chellappa,et al.  Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges , 2016, IEEE Signal Processing Magazine.

[44]  Aythami Morales,et al.  GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning , 2021, ArXiv.

[45]  Fanglin Chen,et al.  Unobtrusive sleep monitoring using smartphones , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[46]  Sin Kit Lo,et al.  A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective , 2020, ArXiv.

[47]  Dan Boneh,et al.  Differentially Private Learning Needs Better Features (or Much More Data) , 2020, ICLR.

[48]  Jesús Francisco Vargas-Bonilla,et al.  Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease , 2019, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).

[49]  Daniel Gatica-Perez,et al.  DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones , 2018, IEEE Transactions on Mobile Computing.

[50]  Arun Ross,et al.  What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics , 2016, IEEE Transactions on Information Forensics and Security.

[51]  Catuscia Palamidessi,et al.  Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.

[52]  Dejing Dou,et al.  Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction , 2016, AAAI.

[53]  Julian Fierrez,et al.  BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks , 2020, IEEE Transactions on Information Forensics and Security.

[54]  Yasushi Makihara,et al.  OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender , 2019, 2019 International Conference on Biometrics (ICB).

[55]  Stanley Robson de Medeiros Oliveira,et al.  Privacy preserving frequent itemset mining , 2002 .

[56]  Keke Chen,et al.  Privacy preserving data classification with rotation perturbation , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[57]  Juan C. Quiroz,et al.  Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study , 2018, JMIR mental health.

[58]  Wouter Joosen,et al.  Data Privatizer for Biometric Applications and Online Identity Management , 2019, Privacy and Identity Management.

[59]  Steven M. Bellovin,et al.  "I don't have a photograph, but you can have my footprints.": Revealing the Demographics of Location Data , 2015, ICWSM.

[60]  M. Hansen,et al.  Participatory Sensing , 2019, Internet of Things.

[61]  Simson L. Garfinkel,et al.  De-Identification of Personal Information , 2015 .

[62]  Emmanuel Agu,et al.  Smartphone Inference of Alcohol Consumption Levels from Gait , 2015, 2015 International Conference on Healthcare Informatics.

[63]  Anil K. Jain,et al.  Soft Biometric Traits for Personal Recognition Systems , 2004, ICBA.

[64]  Xingquan Zhu,et al.  Hashing Techniques: A Survey and Taxonomy , 2017, ACM Comput. Surv..

[65]  Jin Ye,et al.  Motion-To-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users , 2020, Sensors.

[66]  Kamalika Chaudhuri,et al.  When Random Sampling Preserves Privacy , 2006, CRYPTO.

[67]  Sébastien Marcel,et al.  Keystroke Biometrics Ongoing Competition , 2016, IEEE Access.

[68]  Shuigeng Zhou,et al.  A novel privacy preserving method for data publication , 2019, Inf. Sci..

[69]  William E. Winkler,et al.  Multiplicative Noise for Masking Continuous Data , 2001 .

[70]  P. Kostopoulos,et al.  StayActive: An Application for Detecting Stress , 2015 .

[71]  Aythami Morales,et al.  Blockchain in the Internet of Things: Architectures and Implementation , 2020, 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC).

[72]  Richard B. Parker.  A Definition of Privacy , 2017 .

[73]  Martin Raubal,et al.  Correlating mobile phone usage and travel behavior - A case study of Harbin, China , 2012, Comput. Environ. Urban Syst..

[74]  Joydeep Ghosh,et al.  Privacy-preserving distributed clustering using generative models , 2003, Third IEEE International Conference on Data Mining.

[75]  DeraviFarzin,et al.  Touch-dynamics based Behavioural Biometrics on Mobile Devices ? A Review from a Usability and Performance Perspective , 2020 .

[76]  Hao Chen,et al.  TouchLogger: Inferring Keystrokes on Touch Screen from Smartphone Motion , 2011, HotSec.

[77]  L. O'Gorman,et al.  Comparing passwords, tokens, and biometrics for user authentication , 2003, Proceedings of the IEEE.

[78]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.

[79]  Jun Han,et al.  ACCessory: password inference using accelerometers on smartphones , 2012, HotMobile '12.

[80]  Julian Fiérrez,et al.  Exploiting complexity in pen- and touch-based signature biometrics , 2020, International Journal on Document Analysis and Recognition (IJDAR).

[81]  H. Brendan McMahan,et al.  Learning Differentially Private Recurrent Language Models , 2017, ICLR.

[82]  Richard M. Guest,et al.  Predicting sex as a soft-biometrics from device interaction swipe gestures , 2016, Pattern Recognit. Lett..

[83]  Vassilios S. Verykios,et al.  Disclosure limitation of sensitive rules , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[84]  Emin Anarim,et al.  Age group detection using smartphone motion sensors , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[85]  David Eckhoff,et al.  Metrics : a Systematic Survey , 2018 .

[86]  Yufei Tao,et al.  M-invariance: towards privacy preserving re-publication of dynamic datasets , 2007, SIGMOD '07.

[87]  Yoo-Joo Choi,et al.  Augmented-Reality Survey: from Concept to Application , 2017, KSII Trans. Internet Inf. Syst..

[88]  Adam J. Aviv,et al.  Practicality of accelerometer side channels on smartphones , 2012, ACSAC '12.

[89]  Arun Ross,et al.  Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images , 2017, 2018 International Conference on Biometrics (ICB).

[90]  Oscar Mayora-Ibarra,et al.  Mobile phones as medical devices in mental disorder treatment: an overview , 2014, Personal and Ubiquitous Computing.

[91]  Cecilia Mascolo,et al.  Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study , 2017, WWW.

[92]  D. Liu,et al.  Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining , 2018, Pervasive Mob. Comput..

[93]  Vassilios S. Verykios,et al.  Privacy Preserving Blocking and Meta-Blocking , 2015, ECML/PKDD.

[94]  Aythami Morales,et al.  TypeNet: Deep Learning Keystroke Biometrics , 2021, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[95]  Hamed Haddadi,et al.  Privacy Leakage in Mobile Computing: Tools, Methods, and Characteristics , 2014, ArXiv.

[96]  Chris Clifton,et al.  When do data mining results violate privacy? , 2004, KDD.

[97]  Elisa Bertino,et al.  State-of-the-art in privacy preserving data mining , 2004, SGMD.

[98]  Ian Butterworth,et al.  Detection of Motor Impairment in Parkinson's Disease Via Mobile Touchscreen Typing , 2017, IEEE Transactions on Biomedical Engineering.

[99]  Bambang Parmanto,et al.  Systematic Review of Mobile Health Applications in Rehabilitation. , 2019, Archives of physical medicine and rehabilitation.

[100]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[101]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[102]  Aythami Morales,et al.  Assessing the Quality of Swipe Interactions for Mobile Biometric Systems , 2020, 2020 IEEE International Joint Conference on Biometrics (IJCB).

[103]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[104]  Hoeteck Wee,et al.  Toward Privacy in Public Databases , 2005, TCC.

[105]  K. Sarawadekar,et al.  Gender Recognition using in-built Inertial Sensors of Smartphone , 2020, 2020 IEEE REGION 10 CONFERENCE (TENCON).

[106]  David Mohaisen,et al.  Sensor-Based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey , 2021, IEEE Internet of Things Journal.

[107]  Balachander Krishnamurthy,et al.  On the leakage of personally identifiable information via online social networks , 2009, CCRV.

[108]  Naser Damer,et al.  Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination , 2019, 2019 International Conference on Biometrics (ICB).

[109]  Ruth Brand,et al.  Microdata Protection through Noise Addition , 2002, Inference Control in Statistical Databases.

[110]  Aythami Morales,et al.  Child-Computer Interaction: Recent Works, New Dataset, and Age Detection , 2021, ArXiv.

[111]  T. Neal,et al.  Mood Versus Identity: Studying the Influence of Affective States on Mobile Biometrics , 2020, 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020).

[112]  Julian Fiérrez,et al.  MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns , 2019, Advanced Sciences and Technologies for Security Applications.

[113]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[114]  Jano Moreira de Souza,et al.  Towards an observatory for mobile participatory sensing applications , 2017, 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[115]  Flávio du Pin Calmon,et al.  Privacy against statistical inference , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[116]  Pieter H. Hartel,et al.  Putting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources , 2019, Telematics Informatics.

[117]  Lina Yao,et al.  Deep Learning for Sensor-based Human Activity Recognition , 2021, ACM Comput. Surv..

[118]  Zhen Lin,et al.  Using binning to maintain confidentiality of medical data , 2002, AMIA.

[119]  Xiaojiang Chen,et al.  SleepGuard , 2018, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.

[120]  Miguel A. Ferrer,et al.  Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health , 2020, Cognitive Computation.

[121]  M. Köppen,et al.  The Curse of Dimensionality , 2010 .

[122]  Qiang Yang,et al.  Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..

[123]  Moni Naor,et al.  Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.

[124]  Qihui Wu,et al.  A survey of machine learning for big data processing , 2016, EURASIP Journal on Advances in Signal Processing.

[125]  Jun Han,et al.  ACComplice: Location inference using accelerometers on smartphones , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[126]  Raymond Chi-Wing Wong,et al.  (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.

[127]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[128]  Aythami Morales,et al.  BeCAPTCHA: Bot Detection in Smartphone Interaction using Touchscreen Biometrics and Mobile Sensors , 2020, ArXiv.

[129]  Gang Zhou,et al.  Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning , 2021, ACM Trans. Internet Things.

[130]  Michael Riegler,et al.  Mental health monitoring with multimodal sensing and machine learning: A survey , 2018, Pervasive Mob. Comput..

[131]  Tempestt J. Neal,et al.  A gender-specific behavioral analysis of mobile device usage data , 2018, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).

[132]  Patrick Bours,et al.  Studying WiFi and Accelerometer Data Based Authentication Method on Mobile Phones , 2018, ICBEA '18.

[133]  Omer Reingold,et al.  Computational Differential Privacy , 2009, CRYPTO.

[134]  Jung-Hsien Chiang,et al.  Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study , 2016, JMIR research protocols.

[135]  Chris Clifton,et al.  Hiding the presence of individuals from shared databases , 2007, SIGMOD '07.

[136]  Lin Yao,et al.  A sensitive data aggregation scheme for body sensor networks based on data hiding , 2012, Personal and Ubiquitous Computing.

[137]  Philip S. Yu,et al.  A Survey of Randomization Methods for Privacy-Preserving Data Mining , 2008, Privacy-Preserving Data Mining.

[138]  Dhiren R. Patel,et al.  Blocking Based Approach for Classification Rule Hiding to Preserve the Privacy in Database , 2011, 2011 International Symposium on Computer Science and Society.

[139]  Jingyu Hua,et al.  We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones , 2015, IEEE Transactions on Information Forensics and Security.

[140]  Wei Cui,et al.  WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM , 2019, IEEE Transactions on Mobile Computing.

[141]  Philip S. Yu,et al.  A Condensation Approach to Privacy Preserving Data Mining , 2004, EDBT.

[142]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[143]  Adam Glowacz,et al.  Security Framework for IoT Based Real-Time Health Applications , 2021, Electronics.

[144]  Simon Parkinson,et al.  Biometric Systems Utilising Health Data from Wearable Devices , 2020, ACM Comput. Surv..

[145]  Kanishka Bhaduri,et al.  Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[146]  Alexandre V. Evfimievski,et al.  Privacy preserving mining of association rules , 2002, Inf. Syst..

[147]  Arun Ross,et al.  PrivacyNet: Semi-Adversarial Networks for Multi-Attribute Face Privacy , 2020, IEEE Transactions on Image Processing.

[148]  Vivek Kanhangad,et al.  Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[149]  Farzin Deravi,et al.  Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships , 2019, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[150]  Javier Hernandez-Ortega,et al.  Active detection of age groups based on touch interaction , 2018, IET Biom..

[151]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[152]  M Jamal Deen,et al.  Smartphone Sensors for Health Monitoring and Diagnosis , 2019, Sensors.

[153]  Konstantinos Markantonakis,et al.  Location Tracking Using Smartphone Accelerometer and Magnetometer Traces , 2019, ARES.

[154]  Lei Zheng,et al.  DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection , 2017, KDD.

[155]  Qinghua Li,et al.  Efficient and privacy-preserving data aggregation in mobile sensing , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[156]  Wei Jia,et al.  Survey of Gait Recognition , 2009, ICIC.

[157]  Vishal Singh,et al.  Ensemble based real-time indoor localization using stray WiFi signal , 2018, 2018 IEEE International Conference on Consumer Electronics (ICCE).

[158]  Roger Clarke,et al.  Privacy and consumer risks in cloud computing , 2010, Comput. Law Secur. Rev..

[159]  Chris Bevan,et al.  Different strokes for different folks? Revealing the physical characteristics of smartphone users from their swipe gestures , 2016, Int. J. Hum. Comput. Stud..

[160]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.

[161]  Alex Pentland,et al.  Mobile Communication Signatures of Unemployment , 2016, SocInfo.

[162]  Qing Zhang,et al.  Aggregate Query Answering on Anonymized Tables , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[163]  Tianqing Zhu,et al.  Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy , 2018, IEEE Access.

[164]  Klaus David,et al.  6G Vision and Requirements: Is There Any Need for Beyond 5G? , 2018, IEEE Vehicular Technology Magazine.

[165]  Liu Yang,et al.  Inferring demographics from human trajectories and geographical context , 2019, Comput. Environ. Urban Syst..

[166]  Josep Domingo-Ferrer,et al.  From t-closeness to differential privacy and vice versa in data anonymization , 2015, Knowl. Based Syst..

[167]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[168]  Aaron Roth,et al.  Mechanism design in large games: incentives and privacy , 2012, ITCS.

[169]  Xiao Ma,et al.  Data analytics in a privacy-concerned world , 2021, Journal of Business Research.

[170]  Yu Zhang,et al.  Preserving User Location Privacy in Mobile Data Management Infrastructures , 2006, Privacy Enhancing Technologies.

[171]  Ken Barker,et al.  A Data Privacy Taxonomy , 2009, BNCOD.

[172]  Charith Perera,et al.  Privacy Laws and Privacy by Design Schemes for the Internet of Things , 2021, ACM Comput. Surv..

[173]  José María de Fuentes,et al.  Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review , 2020, Sensors.

[174]  Jun Luo,et al.  An effective value swapping method for privacy preserving data publishing , 2016, Secur. Commun. Networks.

[175]  Rama Chellappa,et al.  Cancelable Biometrics: A review , 2015, IEEE Signal Processing Magazine.

[176]  Yanjiao Chen,et al.  Deep Learning on Mobile and Embedded Devices , 2020, ACM Comput. Surv..

[177]  M. Petró‐Turza,et al.  The International Organization for Standardization. , 2003 .

[178]  Chuhan Gao,et al.  Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries , 2019, Proc. Priv. Enhancing Technol..