A Survey of Privacy Vulnerabilities of Mobile Device Sensors

Delgado-Santos, Paula; Stragapede, Giuseppe; Tolosana, Rubén; Guest, Richard; Deravi, Farzin; Vera-Rodríguez, Rubén

doi:10.1145/3510579

Cited by 35 publications

(21 citation statements)

References 163 publications

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“…Given the variety and volume of data collected, it is essential to ensure data privacy and confidentiality. Although the topic of privacy in mobile sensing is a whole issue in itself [ 99 , 100 , 104 , 105 , 106 , 107 , 108 ], few studies have discussed approaches to minimize privacy invasion of users. Data such as audio recordings from the microphone, keyboard presses, and content of text messages are some examples of particularly sensitive data.…”

Section: Resultsmentioning

confidence: 99%

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

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Section: Resultsmentioning

confidence: 99%

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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“…As we observed, the Autoencoder of ECGXtractor was not trained specifically for recognition tasks. Hence, the features extracted from ECG segments are generic, and we expect that they can be successfully exploited in other applications, revealing sensitive information such as age, sex, or medical pathologies [47]. The risk assessment related to this aspect and eventual countermeasures shall be further investigated.…”

Section: Discussionmentioning

confidence: 99%

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

Melzi¹,

Tolosana²,

Vera-Rodríguez³

2022

Preprint

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Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single-and multi-session scenarios. Finally, we also perform single-and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices.In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single-and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.

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“…Mobile security involves many fields. An endless stream of attacks makes researchers pay attention to the research of vulnerabilities in electronic products and smart phones ( Niu et al, 2008 ; Zhou et al, 2016 ; Hur and Shamsi, 2017 ; Delgado-Santos et al, 2021 ). In order to get the optimal solution, bionic algorithms derived from nature often provide a novel research idea ( Liu et al, 2021a ; Xu et al, 2022 ; Zhang et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Spider-Inspired HCCapture: Beware That What You Are Writing on Mobile Devices Is Becoming Prey for Spiders

Zhu

Chen

et al. 2022

Front. Bioeng. Biotechnol.

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On mobile devices, the most important input interface is touchscreen, which can transmit a large amount of sensitive information. Many researchers have proven that sensors can be used as side channels to leak touchscreen interactive information. The research of information leakage in the restricted area has been relatively mature, but in the unrestricted area, still there are two issues to be solved urgently: chirography difference and posture variation. We learn from the way spiders perceive prey through the subtle vibrations of their webs; an unrestricted-area handwriting information speculation framework, called spider-inspired handwriting character capture (spider-inspired HCCapture), is designed. Spider-inspired HCCapture exploits the motion sensor as the side-channel and uses the neural network algorithm to train the recognition model. To alleviate the impact of different handwriting habits, we utilize the generality patterns of characters rather than the patterns of raw sensor signals. Furthermore, each character is disassembled into basic strokes, which are used as recognition features. We also proposed a user-independent posture-aware approach to detect the user’s handwriting posture to select a suitable one from some pretrained models for speculation. In addition, the Markov model is introduced into spider-inspired HCCapture, which is used as an enhancement feature when there is a correlation between adjacent characters. In conclusion, spider-inspired HCCapture completes the handwritten character speculation attack without obtaining the victim’s information in advance. The experimental results show that the accuracy of spider-inspired HCCapture reaches 96.1%.

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A Survey of Privacy Vulnerabilities of Mobile Device Sensors

Cited by 35 publications

References 163 publications

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

Spider-Inspired HCCapture: Beware That What You Are Writing on Mobile Devices Is Becoming Prey for Spiders

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