Abstract
Smart home environment plays a prominent role in improving the quality of life of the residents by enabling home automation, health care and safety through various Internet of Things (IoT) devices. However, a large amount of data generated by sensors in a smart home environment heighten security and privacy concerns among potential users. Some of the data can be sensitive as it contains information about users’ private activities, location, behavioural patterns and health status. Other concerns of the users are towards the distribution and sharing of data to third parties. In this chapter, we propose privacy-enabled smart home framework consisting of three major components: activity recognition and occupancy detection, privacy-preserving data management and voice assistant. The proposed platform includes unobtrusive sensors for multiple occupancy detection and activity recognition. The privacy-enabled voice assistant performs interaction with smart home. We also present a detailed description of system architecture with service middleware.
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References
Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on Explainable Artificial Intelligence (XAI). IEEE Access 6:52138–52160
Ahmad J, Larijani H, Emmanuel R, Mannion M, Javed A (2018) Occupancy detection in non-residential buildings—a survey and novel privacy preserved occupancy monitoring solution. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.12.001
Apthorpe N, Huang DY, Reisman D, Narayanan A, Feamster N (2018) Keeping the smart home private with smart(er) IoT traffic shaping. arXiv preprint arXiv:181200955
Bos JW, Lauter K, Naehrig M (2014) Private predictive analysis on encrypted medical data. J Biomed Inform 50:234–243
Chen H, Liu X, Yin D, Tang J (2017) A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explor Newsl 19(2):25–35
Chen L, Nugent C, Okeyo G (2014) An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans Hum Mach Syst 44(1):92–105
Chen Z, Jiang C, Xie L (2018) Building occupancy estimation and detection: a review. Energy Build 169:260–270
Das A, Degeling M, Wang X, Wang J, Sadeh N, Satyanarayanan M (2017) Assisting users in a world full of cameras: a privacy-aware infrastructure for computer vision applications. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), IEEE, pp 1387–1396
Dwork C (2011) Differential privacy. In: Encyclopedia of cryptography and security, pp 338–340
Fuxreiter T, Mayer C, Hanke S, Gira M, Sili M, Kropf J (2010) A modular platform for event recognition in smart homes. In: The 12th IEEE international conference on e-health networking, applications and services, IEEE, pp 1–6
GDPR (2018) General Data Protection Regulation (GDPR) final text neatly arranged. [online] Available at: https://www.gdpr-info.eu/. Accessed 16 Apr 2019
Holzinger A, Biemann C, Pattichis CS, Kell DB (2017) What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:171209923
Holzinger A, Kieseberg P, Weippl E, Tjoa AM (2018) Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In: International cross-domain conference for machine learning and knowledge extraction. Springer, Cham, pp 1–8
Jia R, Dong R, Sastry SS, Sapnos CJ (2017) Privacy-enhanced architecture for occupancy-based HVAC control. In: 2017 ACM/IEEE 8th international conference on cyber-physical systems (ICCPS), IEEE, pp 177–186
Jung Y (2017) Hybrid-aware model for senior wellness service in smart home. Sensors 17(5):1182
Liu B, Andersen MS, Schaub F, Almuhimedi H, Zhang SA, Sadeh N, Agarwal Y, Acquisti A (2016) Follow my recommendations: a personalized privacy assistant for mobile app permissions. In: Twelfth symposium on usable privacy and security (SOUPS 2016), pp 27–41
Machado E, Singh D, Cruciani F, Chen L, Hanke S, Salvago F, Kropf J, Holzinger A (2018) A conceptual framework for adaptive user interfaces for older adults. In: 2018 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), IEEE, pp 782–787
Merdivan E, Vafeiadis A, Kalatzis D, Henke S, Kropf J, Votis K, Giakoumis D, Tzovaras D, Chen, L, Hamzaoui R, Geist M (2018) Image-based natural language understanding using 2D convolutional neural networks. arXiv preprint arXiv:181010401
Mittelstadt B, Russell C, Wachter S (2018) Explaining explanations in AI. arXiv preprint arXiv:181101439
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S (2015) Human-level control through deep reinforcement learning. Nature 518(7540):p529
MonteriĂą A, Prist M, Frontoni E, Longhi S, Pietroni F, Casaccia S, Scalise L, Cenci A, Romeo L, Berta R, Pescosolido L (2018) A smart sensing architecture for domestic monitoring: methodological approach and experimental validation. Sensors 18(7):p2310
Naehrig M, Lauter K, Vaikuntanathan V (2011) Can homomorphic encryption be practical? In: Proceedings of the 3rd ACM workshop on cloud computing security workshop, ACM, pp 113–124
Okeyo G, Chen L, Wang H (2014) Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes. Future Gener Comput Syst 39:29–43
Park Y, Kang S, Seo J (2018) An efficient framework for development of task-oriented dialog systems in a smart home environment. Sensors 18(5):1581
Pathak M, Rane S, Sun W, Raj B (2011) Privacy preserving probabilistic inference with hidden Markov models. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 5868–5871
Pathak MA, Raj B (2013) Privacy-preserving speaker verification and identification using gaussian mixture models. IEEE Trans Audio Speech Lang Process 21(2):397–406
Pieraccini R, Huerta J (2005) Where do we go from here? Research and commercial spoken dialog systems. In: 6th SIGdial workshop on discourse and dialogue
Psychoula I, Merdivan E, Singh D, Chen L, Chen F, Hanke S, Kropf J, Holzinger A, Geist M (2018) A deep learning approach for privacy preservation in assisted living. In: 2018 IEEE international conference on pervasive computing and communications workshops (PerCom workshops), IEEE, pp 710–715
Psychoula I, Singh D, Chen L, Chen F, Holzinger A, Ning H (2018) Users’ privacy concerns in IoT based applications. In: 2018 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, pp 1887–1894
Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1135–1144
Serban IV, Lowe R, Henderson P, Charlin L, Pineau J (2015) A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:151205742
Singh D, Merdivan E, Hanke S, Kropf J, Geist M, Holzinger A (2017) Convolutional and recurrent neural networks for activity recognition in smart environment. In: Towards integrative machine learning and knowledge extraction. Springer, Cham, pp 194–205
Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A (2017) Human activity recognition using recurrent neural networks. In: International cross-domain conference for machine learning and knowledge extraction. Springer, Cham, pp 267–274
Singh D, Psychoula I, Kropf J, Hanke S, Holzinger A (2018) Users’ perceptions and attitudes towards smart home technologies. In: International conference on smart homes and health telematics. Springer, Cham, pp 203–214
Sivaraman V, Gharakheili HH, Vishwanath A, Boreli R, Mehani O (2015) Network-level security and privacy control for smart-home IoT devices. In: 2015 IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob), IEEE, pp 163–167
Sutskever I, Vinyals O, Le, QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112
Wang D, Yang Q, Abdul A, Lim BY (2019) Designing theory-driven user-centric explainable AI. In: Proceedings of the SIGCHI conference on human factors in computing systems CHI, vol 19
Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recogn Lett 119:3–11
Xie P, Bilenko M, Finley T, Gilad-Bachrach R, Lauter K, Naehrig M (2014) Crypto-nets: neural networks over encrypted data. arXiv preprint arXiv:14126181
Yang J, Zou H, Jiang H, Xie L (2018) Device-free occupant activity sensing using WiFi-enabled IoT devices for smart homes. IEEE Internet Things J 5(5):3991–4002
Zheng S, Apthorpe N, Chetty M, Feamster N (2018) User perceptions of smart home IoT privacy. Proc ACM Hum-Comput Interact 2(CSCW):200
Acknowledgements
This work has been funded by the European Union Horizon2020 MSCA ITN ACROSSING project (GA no. 616757). The authors would like to thank the members of the project’s consortium for their valuable inputs.
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Singh, D. et al. (2020). Privacy-Enabled Smart Home Framework with Voice Assistant. In: Chen, F., GarcĂa-Betances, R., Chen, L., Cabrera-UmpiĂ©rrez, M., Nugent, C. (eds) Smart Assisted Living. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-25590-9_16
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