Helping Users Managing Context-Based Privacy Preferences

Alom, Md. Zulfikar; Carminati, Barbara; Ferrari, Elena

doi:10.1109/scc.2019.00027

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…17 We could imagine providing different sets of defaults to different users, effectively alias starter packs, generated from our data. We see parallels to work that investigates contextual preferences and personalization in information systems (De Amo et al 2015;Stefanidis et al 2011) and privacy research (Wijesekera et al 2018;Alom et al 2019).…”

Section: Contextual Defaultssupporting

confidence: 64%

An empirical investigation of command-line customization

Schröder

Cito

2021

Empir Software Eng

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The interactive command line, also known as the shell, is a prominent mechanism used extensively by a wide range of software professionals (engineers, system administrators, data scientists, etc.). Shell customizations can therefore provide insight into the tasks they repeatedly perform, how well the standard environment supports those tasks, and ways in which the environment could be productively extended or modified. To characterize the patterns and complexities of command-line customization, we mined the collective knowledge of command-line users by analyzing more than 2.2 million shell alias definitions found on GitHub. Shell aliases allow command-line users to customize their environment by defining arbitrarily complex command substitutions. Using inductive coding methods, we found three types of aliases that each enable a number of customization practices: Shortcuts (for nicknaming commands, abbreviating subcommands, and bookmarking locations), Modifications (for substituting commands, overriding defaults, colorizing output, and elevating privilege), and Scripts (for transforming data and chaining subcommands). We conjecture that identifying common customization practices can point to particular usability issues within command-line programs, and that a deeper understanding of these practices can support researchers and tool developers in designing better user experiences. In addition to our analysis, we provide an extensive reproducibility package in the form of a curated dataset together with well-documented computational notebooks enabling further knowledge discovery and a basis for learning approaches to improve command-line workflows.

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Section: Contextual Defaultssupporting

confidence: 64%

An empirical investigation of command-line customization

Schröder

Cito

2021

Empir Software Eng

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“…To cope with the issue of ever-changing contexts, Alom et al [26] proposed a context-based privacy management system that utilizes machine learning algorithms. In their system, privacy preferences for a new context are automatically determined based on existing ones.…”

Section: Discussionmentioning

confidence: 99%

IM2P-Medical: Towards Individual Management Privacy Preferences for the Medical Web Apps

Phien¹,

Phuong²,

Huynh³

et al. 2023

IJACSA

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With the advancement of technology, people are now able to monitor their health more efficiently. Mobile phones and smartwatches are equipped with sensors that can measure real-time changes in blood pressure, SPO2, and other attributes and public them to service providers via web applications (called web apps) for health improvement suggestions. Moreover, users can share the collected health data with other people, such as doctors, relatives, or friends. However, using technology in healthcare has raised the issue of privacy. Some health web apps, by default, intrusively gather and share data. Additionally, smartwatches may monitor people's health status 24/7. Therefore, users want to control how their health is processed (e.g., collected and shared). This can be cumbersome as they would have to configure each device manually. To address this problem, we have developed a privacy-preference prediction mechanism in the web apps called IM2P-Medical: towards Individual Management Privacy Preferences for the Medical web apps. To capture individual privacy preferences in the web apps, our model learns users' privacy behavior based on their responses in different medical scenarios. In practice, we exploited several machine learning algorithms: SVM, Gradient Boosting Classifier, Ada Boost Classifier, and Gradient Boosting Regressor. To prove the effectiveness of the proposed model, we set up several scenarios to measure the accuracy as well as the satisfaction level in the two participant groups (i.e., expert and normal users). One key point in this research's selection of participants is its focus on those living in developing countries, where privacy violation issues are not a common topic. The main contribution of our model is that it allows users to preserve their privacy without configuring privacy settings themselves.

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“…However, since the user's context changes so frequently, it might be a time-consuming and difficult operation to obtain consent for every little change in a DS' privacy preferences. Hence, we can use machine learning and AI techniques which can predict new privacy preferences for a given scenario and set consent for DS [19,20].…”

Section: Privacy Preferences and Policiesmentioning

confidence: 99%

A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Khatiwada,

Yang,

Lin

et al. 2024

IoT

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Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.

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Helping Users Managing Context-Based Privacy Preferences

Cited by 7 publications

References 19 publications

An empirical investigation of command-line customization

An empirical investigation of command-line customization

IM2P-Medical: Towards Individual Management Privacy Preferences for the Medical Web Apps

A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

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