This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K. and Zhang, L. 2016. Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ACM, pp. 308–318.AbadiM.ChuA.GoodfellowI.McMahanH. B.MironovI.TalwarK. and ZhangL.2016Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ACM, pp.308–318Search in Google Scholar
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F. and Marchand, M. 2015. Domain-adversarial neural networks, available at: https://arxiv.org/abs/1412.4446AjakanH.GermainP.LarochelleH.LavioletteF. and MarchandM.2015, available at:https://arxiv.org/abs/1412.4446Search in Google Scholar
Bian, S., Wang, T., Hiromoto, M. and Shi, Y. 2020. ENSEI: efficient secure inference via frequency-domain homomorphic convolution for privacy-preserving visual recognition, available at: https://arxiv.org/abs/2003.05328BianS.WangT.HiromotoM. and ShiY.2020, available at:https://arxiv.org/abs/2003.05328Search in Google Scholar
Bun, M. and Steinke, T. 2016. Concentrated differential privacy: simplifications, extensions, and lower bounds. Theory of Cryptography Conference, Springer, pp. 635–658.BunM. and SteinkeT.2016Theory of Cryptography Conference, Springer, pp.635–658Search in Google Scholar
Butler, D. J., Huang, J., Roesner, F. and Cakmak, M. 2015. The privacy utility tradeoff for remotely tele-operated robots. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 27–34.ButlerD. J.HuangJ.RoesnerF. and CakmakM.2015Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp.27–34Search in Google Scholar
Chattopadhyay, A. and Boult, T. E. 2007. Privacycam: a privacy preserving camera using uclinux on the blackfin DSP, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.ChattopadhyayA. and BoultT. E.20072007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8Search in Google Scholar
Chen, K., Yao, L., Wang, X., Zhang, D., Gu, T., Yu, Z. and Yang, Z. 2018. Interpretable parallel recurrent neural networks with convolutional attentions for multi-modality activity modeling. IJCNN, IEEE, pp. 1–8.ChenK.YaoL.WangX.ZhangD.GuT.YuZ. and YangZ.2018IJCNN, IEEE, pp.1–8Search in Google Scholar
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z. and Liu, Y. 2020. Deep learning for sensor-based human activity recognition: overview, challenges and opportunities, available at: https://arxiv.org/abs/2001.07416ChenK.ZhangD.YaoL.GuoB.YuZ. and LiuY.2020, available at:https://arxiv.org/abs/2001.07416Search in Google Scholar
Cormode, G., Procopiuc, C. M., Srivastava, D. and Tran, T. T. L. 2012. Differentially private summaries for sparse data. International Conference on Database Theory, pp. 299–311.CormodeG.ProcopiucC. M.SrivastavaD. and TranT. T. L.2012International Conference on Database Theory, pp.299–311Search in Google Scholar
Dai, J., Saghafi, B., Wu, J., Konrad, J. and Ishwar, P. 2015. Towards privacy-preserving recognition of human activities. 2015 IEEE International Conference on In Image Processing (ICIP), pp. 4238–4242.DaiJ.SaghafiB.WuJ.KonradJ. and IshwarP.20152015 IEEE International Conference on In Image Processing (ICIP), pp.4238–4242Search in Google Scholar
Dwork, C. 2008. Differential privacy: a survey of results. TAMC, Springer, pp. 1–19.DworkC.2008TAMC, Springer, pp.1–19Search in Google Scholar
Edwards, H. and Storkey, A. 2016. Censoring representations with an adversary. Proceedings of ICLR, San Juan, Puerto Rico, May 2–4.EdwardsH. and StorkeyA.2016Proceedings of ICLRSan Juan, Puerto Rico, May 2–4Search in Google Scholar
El-Yahyaoui, A. and Ech-Cherif El Kettani, M. D. 2019. A verifiable fully homomorphic encryption scheme for cloud computing security. Technologies 7(21): 1–15.El-YahyaouiA. and Ech-Cherif El KettaniM. D.2019A verifiable fully homomorphic encryption scheme for cloud computing security7(21)1–15Search in Google Scholar
Ertin, E., Stohs, N., Kumar, S., Raij, A., al’Absi, M. and Shah, S. 2011. Autosense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, ACM, pp. 274–287.ErtinE.StohsN.KumarS.RaijA.al’AbsiM. and ShahS.2011Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, ACM, pp.274–287Search in Google Scholar
FacialNetwork.com, Nametag application, available at: http://www.nametag.ws/FacialNetwork.comavailable at:http://www.nametag.ws/Search in Google Scholar
Fontaine, C. and Galand, F. 2007. A survey of homomorphic encryption for nonspecialists. EURASIP Journal on Information Security 2007(1): 013801.FontaineC. and GalandF.2007A survey of homomorphic encryption for nonspecialists2007(1):013801Search in Google Scholar
Gajjar, V., Khandhediya, Y. and Gurnani, A. 2017. Human detection and tracking for video surveillance a cognitive science approach. IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, pp. 2805–2809, doi: 10.1109/ICCVW.2017.330.GajjarV.KhandhediyaY. and GurnaniA.2017IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, pp.2805–2809doi: 10.1109/ICCVW.2017.330Search in Google Scholar
Garcia, F. D. and Jacobs, B. 2010. Privacy-friendly energy-metering via homomorphic encryption. International Workshop on Security and Trust Management, Springer, pp. 226–238.GarciaF. D. and JacobsB.2010International Workshop on Security and Trust Management, Springer, pp. 226–238Search in Google Scholar
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P. and Riviere, E. 2015. Edge-centric computing: vision and challenges. ACM SIGCOMM Computer Communication Review 45(5): 37–42.Garcia LopezP.MontresorA.EpemaD.DattaA.HigashinoT.IamnitchiA.BarcellosM.FelberP. and RiviereE.2015Edge-centric computing: vision and challenges45(5):37–42Search in Google Scholar
Gomathisankaran, M., Yuan, X. and Kamongi, P. 2013. Ensure privacy and security in the process of medical image analysis. 2013 IEEE International Conference on Granular Computing (GrC), pp. 120–125.GomathisankaranM.YuanX. and KamongiP.20132013 IEEE International Conference on Granular Computing (GrC), pp.120–125Search in Google Scholar
Haris, M., Haddadi, H. and Hui, P. 2014. Privacy leakage in mobile computing: tools, methods, and characteristics, available at: https://arxiv.org/abs/1410.4978.HarisM.HaddadiH. and Hui,P.2014, available at:https://arxiv.org/abs/1410.4978Search in Google Scholar
Hayes, J. and Ohrimenko, O. 2018. Contamination attacks and mitigation in multi-party machine learning. Conference on Neural Information Processing Systems (NeurIPS), pp. 6602–6614.HayesJ. and OhrimenkoO.2018Conference on Neural Information Processing Systems (NeurIPS), pp.6602–6614Search in Google Scholar
Hu, C., Chen, Y., Peng, X., Yu, H., Gao, C. and Hu, L. 2019. A novel feature incremental learning method for sensor-based activity recognition. IEEE Transactions on Knowledge and Data Engineering 31(6): 1038–1050.HuC.ChenY.PengX.YuH.GaoC. and HuL.2019A novel feature incremental learning method for sensor-based activity recognition31(6):1038–1050Search in Google Scholar
Hwang, S., Park, J., Kim, N., Choi, Y. and Kweon, I. S. 2015. Multispectral pedestrian detection: benchmark dataset and baseline. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 7-12, pp. 1037–1045.HwangS.ParkJ.KimN.ChoiY. and KweonI. S.2015Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern RecognitionJune 7-12, pp.1037–1045Search in Google Scholar
Iwasawa, Y., Nakayama, K., Yairi, I. E. and Matsuo, Y. 2017. Privacy issues regarding the application of DNNs to activity-recognition using wearables and its countermeasures by use of adversarial training. International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 1930–1936.IwasawaY.NakayamaK.YairiI. E. and MatsuoY.2017International Joint Conference on Artificial Intelligence (IJCAI-17), pp.1930–1936Search in Google Scholar
Jain, A. and Kanhangad, V. 2016. Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings. ICCTICT, IEEE, pp. 597–602.JainA. and KanhangadV.2016ICCTICT, IEEE, pp.597–602Search in Google Scholar
Juuti, M., Szyller, S., Marchal, S. and Asokan, N. 2019. PRADA: protecting against DNN model stealing attacks. 2019 IEEE European Symposium on Security and Privacy (EuroS&P), Stockholm, pp. 512–527.JuutiM.SzyllerS.MarchalS. and AsokanN.20192019 IEEE European Symposium on Security and Privacy (EuroS&P), Stockholm, pp.512–527Search in Google Scholar
Kariyappa, S. and Kariyappa, S. 2019. Defending against model stealing attacks with adaptive misinformation, available at: https://arxiv.org/abs/1911.07100KariyappaS. and KariyappaS.2019, available at:https://arxiv.org/abs/1911.07100Search in Google Scholar
Liu, F. 2019. Generalized Gaussian mechanism for differential privacy. IEEE TKDE 31(4): 747–756.LiuF.2019Generalized Gaussian mechanism for differential privacy31(4):747–756Search in Google Scholar
Lu, J., Wang, G. and Moulin, P. 2013. Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE TIFS 9(1): 51–61.LuJ.WangG. and MoulinP.2013Human identity and gender recognition from gait sequences with arbitrary walking directions9(1):51–61Search in Google Scholar
Malekzadeh, M., Clegg, R. G., Cavallaro, A. and Haddadi, H. 2018. Protecting sensory data against sensitive inferences. Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, ACM, p. 2.MalekzadehM.CleggR. G.CavallaroA. and HaddadiH.2018Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, ACM, p.2Search in Google Scholar
Malekzadeh, M., Clegg, R. G., Cavallaro, A. and Haddadi, H. 2019. Mobile sensor data anonymization. Proceedings of the International Conference on Internet of Things Design and Implementation, pp. 49–58.MalekzadehM.CleggR. G.CavallaroA. and HaddadiH.2019Proceedings of the International Conference on Internet of Things Design and Implementation, pp.49–58Search in Google Scholar
Malinowski, E. (2010). Adidas miCoach app sets sights square on nike+. Wired Magazine, available at: https://www.wired.com/2010/08/adidas-micoach-app/MalinowskiE.2010.Wired Magazine, available at:https://www.wired.com/2010/08/adidas-micoach-app/Search in Google Scholar
Melis, L., Song, C., De Cristofaro, E. and Shmatikov, V. 2018. Exploiting unintended feature leakage in collaborative learning, available at: https://arxiv.org/abs/1805.04049MelisL.SongC.De CristofaroE. and ShmatikovV.2018, available at:https://arxiv.org/abs/1805.04049Search in Google Scholar
Nasr, M., Shokri, R. and Houmansadr, A. 2018. Machine learning with membership privacy using adversarial regularization. ACM Conference on Computer and Communications Security (CCS), Toronto, Canada, October 15–19.NasrM.ShokriR. and HoumansadrA.2018ACM Conference on Computer and Communications Security (CCS)Toronto, Canada, October 15–19Search in Google Scholar
Nelus, A. and Martin, R. 2019. Privacy-aware feature extraction for gender discrimination versus speaker identification, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12–17.NelusA. and MartinR.2019IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12–17Search in Google Scholar
Nike, Nike+ running, available at: http://www.nike.com/us/enus/c/running/nikeplus/gps-appNikeavailable at:http://www.nike.com/us/enus/c/running/nikeplus/gps-appSearch in Google Scholar
Osia, S. A., Taheri, A., Shamsabadi, A. S., Katevas, K., Haddadi, H. and Rabiee, H. R. 2018. Deep private-feature extraction, available at: https://arxiv.org/abs/1802.03151OsiaS. A.TaheriA.ShamsabadiA. S.KatevasK.HaddadiH. and RabieeH. R.2018, available at:https://arxiv.org/abs/1802.03151Search in Google Scholar
Osia, S. A., Taheri, A., Shamsabadi, A. S., Katevas, K., Haddadi, H. and Rabiee, H. R. 2020. Deep private-feature extraction. IEEE Transactions on Knowledge and Data Engineering 32(1): 54–66.OsiaS. A.TaheriA.ShamsabadiA. S.KatevasK.HaddadiH. and RabieeH. R.2020Deep private-feature extraction32(1):54–66Search in Google Scholar
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I. and Talwar, K. 2017. Semi-supervised knowledge transfer for deep learning from private training data. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, April 24–26.PapernotN.AbadiM.ErlingssonU.GoodfellowI. and TalwarK.2017Proceedings of the International Conference on Learning Representations (ICLR)Toulon, France, April 24–26Search in Google Scholar
Phan, N., Wang, Y., Wu, X. and Dou, D. 2016. Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, February 12–17.PhanN.WangY.WuX. and DouD.2016Thirtieth AAAI Conference on Artificial IntelligencePhoenix, AZ, February 12–17Search in Google Scholar
Ren, S., He, K., Girshick, R. and Sun, J. 2016. Faster R-CNN: towards real-time object detection with region proposal networks, available at: https://arxiv.org/abs/1506.01497RenS.HeK.GirshickR. and SunJ.2016, available at:https://arxiv.org/abs/1506.01497Search in Google Scholar
Ren, Z., Lee, Y. J. and Ryoo, M. S. 2018. Learning to anonymize faces for privacy preserving action detection. European Conference on Computer Vision (ECCV), 620–636.RenZ.LeeY. J. and RyooM. S.2018European Conference on Computer Vision (ECCV)620–636Search in Google Scholar
Ryoo, M. S., Rothrock, B., Fleming, C. and Yang, H. J. 2017. Privacy-preserving human activity recognition from extreme low resolution. AAAI Conference on Artificial Intelligence, San Francisco, CA, February 4–9.RyooM. S.RothrockB.FlemingC. and YangH. J.2017AAAI Conference on Artificial IntelligenceSan Francisco, CA, February 4–9Search in Google Scholar
Shokri, R., Stronati, M., Song, C. and Shmatikov, V. 2017. Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP), pp. 3–18.ShokriR.StronatiM.SongC. and ShmatikovV.20172017 IEEE Symposium on Security and Privacy (SP), pp.3–18Search in Google Scholar
Song, C. and Shmatikov, V. 2020. Overlearning reveals sensitive attributes, Proceedings of International Conference on Learning Representations (ICLR). Virtual Conference, April 26–30.SongC. and ShmatikovV.2020Proceedings of International Conference on Learning Representations (ICLR). Virtual Conference, April 26–30Search in Google Scholar
Song, L., Shokri, R. and Mittal, P. 2019. Membership inference attacks against adversarially robust deep learning models. IEEE Security and Privacy Workshops (SPW).SongL.ShokriR. and MittalP.2019IEEE Security and Privacy Workshops (SPW)Search in Google Scholar
Speciale, P., Schonberger, J. L., Kang, S. B., Sinha, S. N. and Pollefeys, M. 2019. Privacy preserving image-based localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5493–5503.SpecialeP.SchonbergerJ. L.KangS. B.SinhaS. N. and PollefeysM.2019Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5493–5503Search in Google Scholar
Tanuwidjaja, H. C., Choi, R. and Kim, K. 2019. A survey on deep learning techniques for privacy-preserving, machine learning for cyber security. ML4CS 2019. Lecture Notes in Computer Science 11806: 29–46.TanuwidjajaH. C.ChoiR. and KimK.2019A survey on deep learning techniques for privacy-preserving, machine learning for cyber security. ML4CS 20191180629–46Search in Google Scholar
Tramer, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D. and McDaniel, P. 2018. Ensemble adversarial training: attacks and defenses Proceedings of International Conference on Learning Representations (ICLR). Vancouver, Canada, April 30–May 3.TramerF.KurakinA.PapernotN.GoodfellowI.BonehD. and McDanielP.2018Proceedings of International Conference on Learning Representations (ICLR). Vancouver, Canada, April 30–May 3Search in Google Scholar
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K. and Ristenpart, T. 2016. Stealing machine learning models via prediction APIs. USENIX Security Symposium, pp. 601–618.TramèrF.ZhangF.JuelsA.ReiterM. K. and RistenpartT.2016USENIX Security Symposium, pp.601–618Search in Google Scholar
Wang, B. and Gong, N. Z. 2018. Stealing hyperparameters in machine learning. IEEE Symposium on Security and Privacy, Hyatt Regency, San Francisco, May 21–23.WangB. and GongN. Z.2018IEEE Symposium on Security and PrivacyHyatt Regency, San Francisco, May 21–23Search in Google Scholar
Wang, J., Chen, Y., Hao, S., Peng, X. and Hu, L. 2019. Deep learning for sensor-based activity recognition: a survey. Pattern Recognition Letters 119: 3–11.WangJ.ChenY.HaoS.PengX. and HuL.2019Deep learning for sensor-based activity recognition: a survey1193–11Search in Google Scholar
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D. and Campbell, A. T. 2014. Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, pp. 3–14.WangR.ChenF.ChenZ.LiT.HarariG.TignorS.ZhouX.Ben-ZeevD. and CampbellA. T.2014Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, pp.3–14Search in Google Scholar
Winkler, T., Erd´elyi, A. and Rinner, B. 2014. TrustEYE. m4: protecting the sensor-not the camera. 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 159–164.WinklerT.Erd´elyiA. and RinnerB.20142014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.159–164Search in Google Scholar
Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., OBrien, D. R., Steinke, T. and Vadhan, S. 2018. Differential privacy: a primer for a non-technical audience. Vanderbilt Journal of Entertainment & Technology Law 21(1): 209–275.WoodA.AltmanM.BembenekA.BunM.GaboardiM.HonakerJ.NissimK.OBrienD. R.SteinkeT. and VadhanS.2018Differential privacy: a primer for a non-technical audience21(1):209–275Search in Google Scholar
Wu, Z., Wang, Z., Wang, Z. and Jin, H. 2018. Towards privacy-preserving visual recognition via adversarial training: a pilot study, Proceedings of the European Conference on Computer Vision (ECCV), pp. 606–624.WuZ.WangZ.WangZ. and JinH.2018Proceedings of the European Conference on Computer Vision (ECCV), pp.606–624Search in Google Scholar
Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J. and Lv, W. 2019. Edge computing security: state of the art and challenges. Proceedings of the IEEE 107(8): 1608–1631.XiaoY.JiaY.LiuC.ChengX.YuJ. and LvW.2019Edge computing security: state of the art and challenges107(8):1608–1631Search in Google Scholar
You, C.-W., Montes-de Oca, M., Bao, T. J., Lane, N. D, Lu, H., Cardone, G., Torresani, L. and Campbell, A. T. 2012. Carsafe: a driver safety app that detects dangerous driving behavior using dual-cameras on smartphones. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, ACM, pp. 671–672.YouC.-W.Montes-de OcaM.BaoT. J.LaneN. DLuH.CardoneG.TorresaniL. and CampbellA. T.2012Proceedings of the 2012 ACM Conference on Ubiquitous Computing, ACM, pp.671–672Search in Google Scholar
Zhang, D., Yao, L., Chen, K., Long, G. and Wang, S. 2019. Collective protection: preventing sensitive inferences via integrative transformation. 19th IEEE International Conference on Data Mining (ICDM), IEEE, pp. 1–6.ZhangD.YaoL.ChenK.LongG. and WangS.201919th IEEE International Conference on Data Mining (ICDM), IEEE, pp.1–6Search in Google Scholar