Temporal and cultural limits of privacy in smartphone app usage

Sekara, Vedran; Alessandretti, Laura; Mones, Enys; Jönsson, Håkan

doi:10.1038/s41598-021-82294-1

Cited by 14 publications

(24 citation statements)

References 30 publications

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“…Moreover, they found that even after restricting to 60 most frequent apps, it is still possible to uniquely find out 99.4% users. Surprisingly, few other studies [10,11,35] showed that even with 4 random apps, most users can be uniquely re-identified which is a threat to privacy. However, this re-identification varies by season, country [10], mobility, activity in social networks, and gender [11].…”

Section: 3mentioning

confidence: 97%

“…Surprisingly, few other studies [10,11,35] showed that even with 4 random apps, most users can be uniquely re-identified which is a threat to privacy. However, this re-identification varies by season, country [10], mobility, activity in social networks, and gender [11]. Therefore, distinct characteristics regarding fingerprints on apps need to be considered to protect privacy of the users.…”

Section: 3mentioning

confidence: 97%

“…The fingerprint on the used set of apps [10,11]. Here, we used the word fingerprint to denote the touch on the screen of smartphones while using an app.…”

Section: App Usage Fingerprintmentioning

confidence: 99%

“…Possibility to re-identify [10,11] a participant. For example, in this study, we used app usage data to present the re-identification of the depressed and non-depressed students, without having any other information (e.g., demographic characteristics).…”

Section: Re-identificationmentioning

confidence: 99%

“…Scholarly studies [20-22, 29, 33, 41-44, 47] have been conducted regarding depression and technology. A substantial amount of studies [6,7,10,11,14,30,32,35,40] also presented the approach to re-identify. Depressed people may have identifiable unique app signatures as previous studies show different preferences of the depressed.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

Ahmed¹,

Ahmed²

2022

Preprint

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Growing research on re-identification through app usage behavior reveals the privacy threat in having smartphone usage data to third parties. However, re-identifiability of a vulnerable group like the depressed is unexplored. We fill this knowledge gap through an in the wild study on 100 students’ PHQ-9 scale’s data and 7 days’ logged app usage data. We quantify the uniqueness and re-identifiability through exploration of minimum hamming distance in terms of the set of used apps. Our findings show that using app usage data, each of the depressed and non-depressed students is re-identifiable. In fact, using only 7 hours’ data of a week, on average, 91% of the depressed and 88% of the non-depressed are re-identifiable. Moreover, data of a single app category (i.e., Tools) can also be used to re-identify each depressed student. Furthermore, we find that the rate of uniqueness among the depressed students is significantly higher in some app categories. For instance, in the Social Media category, the rate of uniqueness is 9% higher (P=.02, Cohen's d=1.31) and in the Health & Fitness category, this rate is 8% higher (P=.005, Cohen’s d=1.47) than the non-depressed group. Our findings suggest that each of the depressed students has a unique app signature which makes them re-identifiable. Therefore, during the design of the privacy protecting systems, designers need to consider the uniqueness of them to ensure better privacy for this vulnerable group.

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Section: 3mentioning

confidence: 97%

Section: 3mentioning

confidence: 97%

“…The fingerprint on the used set of apps [10,11]. Here, we used the word fingerprint to denote the touch on the screen of smartphones while using an app.…”

Section: App Usage Fingerprintmentioning

confidence: 99%

Section: Re-identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

Ahmed¹,

Ahmed²

2022

Preprint

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Pattern unlocking guided multi‐modal continuous authentication for smartphone with multi‐branch context‐aware representation learning and auto encoder

Yao,

Jin,

Gao

et al. 2023

Trans Emerging Tel Tech

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Widely accepted explicit authentication protocols are vulnerable to a series of attacks, for example, shoulder surfing and smudge attacks, leaving users with the constant burden of periodic password changes. As such, we propose a novel framework for continuous authentication on smartphones. This approach is guided by pattern unlocking, which is widely used and will not cause learning cost. After collecting multi‐modal data that describe both behavioral and contextual information, we employ a multi‐branch context‐aware attention network as the representation learner to perform feature extraction, then an auto encoder is then used for authentication. To overcome challenges, including cold‐start and few‐shot training, which is less discussed in other works, we incorporate transfer learning with a coarse‐to‐fine pre‐training workflow. Additionally, we deploy a hierarchical approach to offload model tuning overhead from smartphones. Extensive experiments on more than 68 000 real‐world recordings validate the effectiveness of the proposed method, with an EER (equal error rate) of 2.472% under mixed contexts, which consistently outperforms state‐of‐the‐art approaches under both static and mixed contexts.

show abstract

Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

Ahmed

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Growing research on re-identification through app usage behavior reveals the privacy threat in having smartphone usage data to third parties. However, re-identifiability of a vulnerable group like the depressed is unexplored. We fill this knowledge gap through an in the wild study on 100 students' PHQ-9 scale's data and 7 days' logged app usage data. We quantify the uniqueness and re-identifiability through exploration of minimum hamming distance in terms of the set of used apps. Our findings show that using app usage data, each of the depressed and non-depressed students is re-identifiable. In fact, using only 7 h' data of a week, on average, 91% of the depressed and 88% of the non-depressed are re-identifiable. Moreover, data of a single app category (i.e., Tools) can also be used to re-identify each depressed student. Furthermore, we find that the rate of uniqueness among the depressed students is significantly higher in some app categories. For instance, in the Social Media category, the rate of uniqueness is 9% higher (P = .02, Cohen's d = 1.31) and in the Health & Fitness category, this rate is 8% higher (P = .005, Cohen's d = 1.47) than the non-depressed group. Our findings suggest that each of the depressed students has a unique app signature which makes them re-identifiable. Therefore, during the design of the privacy protecting systems, designers need to consider the uniqueness of them to ensure better privacy for this vulnerable group.

show abstract

Temporal and cultural limits of privacy in smartphone app usage

Cited by 14 publications

References 30 publications

Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

Pattern unlocking guided multi‐modal continuous authentication for smartphone with multi‐branch context‐aware representation learning and auto encoder

Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps

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