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A review of privacy-preserving human and human activity recognition

Im Y. Jung
Im Y. Jung
   | 25 mai 2020
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Research-Article
Publié en ligne: 25 mai 2020
Pages: 1 - 13
Reçu: 30 mars 2020
DOI: https://doi.org/10.21307/ijssis-2020-008
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© 2020 Im Y. Jung published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

A machine learning process (Osia et al., 2018).
A machine learning process (Osia et al., 2018).

Figure 2:

Privacy issues during the machine learning process in Figure 1.
Privacy issues during the machine learning process in Figure 1.

Figure 3:

An inference attack model against collaborative learning (Melis et al., 2018).
An inference attack model against collaborative learning (Melis et al., 2018).

Figure 4:

Object detection (Ren et al., 2016).
Object detection (Ren et al., 2016).

Figure 5:

Sensor-based human activity recognition (Hu et al., 2019).
Sensor-based human activity recognition (Hu et al., 2019).

Figure 6:

Homomorphic encryption (El-Yahyaoui and Ech-Cherif El Kettani, 2019).
Homomorphic encryption (El-Yahyaoui and Ech-Cherif El Kettani, 2019).

Figure 7:

Sensitive areas are encrypted in the image (Chattopadhyay and Boult, 2007).
Sensitive areas are encrypted in the image (Chattopadhyay and Boult, 2007).

Figure 8:

Anonymized images: different modified pictures of the same person (Ren et al., 2018).
Anonymized images: different modified pictures of the same person (Ren et al., 2018).

Figure 9:

A cartooning image (Winkler et al., 2014).
A cartooning image (Winkler et al., 2014).

Figure 10:

A user privacy protection in image-based localization (Speciale et al., 2019).
A user privacy protection in image-based localization (Speciale et al., 2019).

Figure 11:

Central computing versus edge computing.
Central computing versus edge computing.

Figure 12:

Differential privacy (Wood et al., 2018).
Differential privacy (Wood et al., 2018).

Figure 13:

The private-feature extraction framework (Osia et al., 2020).
The private-feature extraction framework (Osia et al., 2020).

Figure 14:

Collective protection of all sensitive information at once (Zhang et al., 2019).
Collective protection of all sensitive information at once (Zhang et al., 2019).

Figure 15:

Model extraction attack (Wang and Gong, 2018).
Model extraction attack (Wang and Gong, 2018).

Human activity recognition.

Research Object Recognition methods Data Application domain Privacy issue
Iwasawa et al. (2017) You et al. (2012) Human activity Deep learning Sensor data from smart wearable devices Daily activity investigation Training data privacyUser data privacy
Chen et al. (2018) Hu et al. (2019) Zhang et al. (2019) Physical activities such as walking and running Deep learning Time series sensor data from smart wearable devices Daily activity investigation User data privacy
Phan et al. (2016) Human activity Deep learning Physical activities, biomarkers, biometric measures Health social network Training data privacy

Privacy concerns in vision-based machine learning.

Research Application domain Privacy concerns
Chattopadhyay and Boult (2007) Intelligent surveillance system Conflict between the purpose of intelligent surveillance systems and the privacy of individuals
Wu et al. (2018) Smart camera application Private information leakage during device-captured visual data upload to centralized cloud for analysis
Gomathisankaran et al. (2013) Medical image analysis on the Cloud Private information leakage of medical data transmitted in the network and processed in the cloud
Shokri et al. (2017) ‘Machine learning as a service’ provided by Google and Amazon Information leakage about training datasets
Speciale et al. (2019) Augmented/Mixed reality (AR/MR) and autonomous robotic system Confidential information disclosure about captured 3D scene

Privacy-preserving approaches.

Research Privacy issue Privacy-preserving approach Protected object
Garcia and Jacobs (2010) Fontaine and Galand (2007) Gomathisankaran et al. (2013) Chattopadhyay and Boult (2007) Private information leakage (Public dataset privacy) (User data privacy) Cryptography Private information (medical image, lifestyle, financial information, face, private location, biometric information, disease information), Human activity (daily life activity, movement)
Butler et al. (2015) Dai et al. (2015) Ryoo et al. (2017) Ren et al. (2018) Winkler et al. (2014) Speciale et al. (2019) Private information leakage (Public dataset privacy)(User data privacy) Anonymized videos
Garcia Lopez et al. (2015) Private information leakage from database (Public dataset privacy)(User data privacy) Local processing
Liu (2019) Bun and Steinke (2016) Information leakage from large-scale database (Public dataset privacy) Differential privacy
Bian et al. (2020) Information leakage in visual recognition(Public dataset privacy)(Training data privacy) Secure inference by homomorphic encryptionSecret sharingHomomorphic convolution
Iwasawa et al. (2017) Ajakan et al. (2015) Edwards and Storkey (2016) Malekzadeh et al. (2018, 2019) Osia et al. (2020) Information disclosure by unintentional discriminating of user information during deep learning (Training data privacy) Adversarial training
Zhang et al. (2019) Adversarial training which is effective on particular sensitive attributes (Training data privacy) Image style transformation
Phan et al. (2016) Abadi et al. (2016) Papernot et al. (2017) Information leakage during deep learning (Training data privacy) Differential privacy
Tramèr et al. (2016) Wang and Gong (2018) Juuti et al. (2019) Kariyappa and Kariyappa (2019) Information leakage during deep learning (Model privacy) Analyze attacker’s queries, Defense against attacks

Human recognition.

Research Object Recognition methods Data Application domain Privacy issue
Chattopadhyay and Boult (2007) Wu et al. (2018) Facial Network, Ren et al. (2016) Human, object Deep learning Image, video Video surveillance Public dataset privacyUser data privacy
Song and Shmatikov (2020) Nelus and Martin (2019) Human face Deep learning Image Binary gender classification Model privacy
Haris et al. (2014) Gajjar et al. (2017) Nike, Malinowski (2010) Human location Deep learning Sensor data Location-based services, mobile heath applications Public dataset privacyUser data privacy
Gomathisankaran et al. (2013) Wang et al. (2014) Ertin et al. (2011) Human disease, human health Deep learning Clinical records, image Medical care Public dataset privacyUser data privacy
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