Security and Privacy Analysis of Employee Monitoring Applications
Notice bibliographique
Résumé
Workplace surveillance is not a new issue; however, recently there has been increasing adoption of Employee Monitoring Applications (EMAs) that observe employees' digital behaviour. \nThis trend was advanced by the increase of remote work due to the COVID-19 pandemic and the ease of deployment of EMAs with the accelerating cloud computing industry. \nEMAs allow employers to monitor their workers' behaviours remotely, resulting in privacy concerns. \n \nEMAs use highly privileged functions to achieve their features, such as web browsing monitoring, key-logging, microphone monitoring, webcam monitoring, and remote takeover of the device. \nEMA vendors claim to protect company security and employee privacy. \nOur research challenge is to assess how well the vendors uphold their claims of protecting security and privacy. \n \nWe develop a framework to assess security and privacy issues related to EMAs. \nOur framework applies dynamic and static analysis techniques to ten popular Windows EMAs. \nEMAs typically have a monitoring app, which is installed on an employee computer. \nThe app collects and sends data to the backend server, which aggregates the data and displays it in a dashboard. \nThe employer has access to the dashboard to view the collected data and configure monitoring settings. \n \nOur app-centred analysis is focused on issues such as insecure data transmissions, lack of certificate pinning, residual vulnerabilities after app un-installation, security vulnerabilities due to use of a proxy, anti-keylogging, conforming to Windows privacy permissions, effectiveness of EMA privacy features, and determining a general monitoring profile. \nThe app-centred analysis informs us whether EMAs are secure at the local and network levels. \nWe also assess whether EMAs uphold their promises in regards to privacy. \n \n \nOur backend analysis focuses on issues like password security, lack of input validation, open cloud storage, insufficient access control, server geolocation, and insecure security configurations like no HSTS enforcement and out-of-date TLS versions. \nAnalysing the backend infrastructure tells us on EMAs' vulnerability posture in regards to a remote attacker threat. \nWe assess whether EMA vendors adequately protect the data they collect about employees. \n \nOur analysis reveals a number of security and privacy vulnerabilities. \nThese vulnerabilities include issues like data creep, where apps collect metadata about employees and their devices, but do not display this data on the dashboard to an employer. \nWe also notice that one app does not use TLS for data transmission, so it sends private employee data over the public Internet for anyone to eavesdrop. \nOne app offers a GDPR mode, which claims to stop collecting highly sensitive data like web browsing history and screenshots. \nHowever, we see that this app still collects and sends web browsing history while this mode is turned on. \nBackend security misconfigurations we observe include open cloud storage, weak password requirements, lack of password guess rate limiting, and no HSTS enforcement. \n \nOverall, we find that each app in our analysis is vulnerable to at least one threat we assess in our framework. \nOur study aims to provide data for legal analysis to assess the need for legal protections for employees against this kind of monitoring.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».