URetail: Privacy User Interfaces for Intelligent Retail Stores

Raber, Frederic; Vossebein, Nils

doi:10.1007/978-3-319-68059-0_54

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…This trend naturally leads to the search for solutions that give users the choice of what data they can access in the Internet space. In particular, article [12] presents the interface URetail in the form of radar, allowing the user to choose which of his/her personal data can be disclosed. However, the implementation of this approach is narrowly focused on the data collected in retail when shopping in online stores.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of an algorithm to protect user communication devices against data leaks

Zadereyko

Prokop

Trofymenko

et al. 2021

EEJET

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In order to identify ways used to collect data from user communication devices, an analysis of the interaction between DNS customers and the Internet name domain space has been carried out. It has been established that the communication device's DNS traffic is logged by the DNS servers of the provider, which poses a threat to the privacy of users. A comprehensive algorithm of protection against the collection of user data, consisting of two modules, has been developed and tested. The first module makes it possible to redirect the communication device's DNS traffic through DNS proxy servers with a predefined anonymity class based on the proposed multitest. To ensure a smooth and sustainable connection, the module automatically connects to a DNS proxy server that has minimal response time from those available in the compiled list. The second module blocks the acquisition of data collected by the developers of the software installed on the user's communication device, as well as by specialized Internet services owned by IT companies. The proposed algorithm makes it possible for users to choose their preferred level of privacy when communicating with the Internet space, thereby providing them with a choice of privacy level and, as a result, limiting the possibility of information manipulation over their owners. The DNS traffic of various fixed and mobile communication devices has been audited. The analysis of DNS traffic has enabled to identify and structure the DNS requests responsible for collecting data from users by the Internet services owned by IT companies. The identified DNS queries have been blocked; it has been experimentally confirmed that the performance of the basic and application software on communication devices was not compromised.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of an algorithm to protect user communication devices against data leaks

Zadereyko

Prokop

Trofymenko

et al. 2021

EEJET

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“…Creating these lists manually comes at a significantly increased users burden [5]. Radar interfaces have been proven to be highly effective for such tasks, like getting an overview on data shared inside an intelligent retail store [7]. In our work, we try to adapt this metaphor on the domain of social network audience selection, which introduces one major obstacle, namely the high amount of data items (firends) to display in the limited space of the UI.…”

Section: Related Workmentioning

confidence: 99%

OmniWedges: Improved Radar-Based Audience Selection for Social Networks

Raber

Krüger

2019

Human-Computer Interaction – INTERACT 2019

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Selecting the right audience for Facebook posts is a task that users often skip, resulting in unwanted post disclosure or avoidance of sharing sensitive posts. We present OmniWedges, a user interface designed to allow users of online social networks to make meaningful decisions on who to share their posts with. Our study results also show that with all Facebook friends, the error rate can be significantly reduced compared to the Facebook interface. In an interview, we were also able to spot a change in posting behavior and frequency with our interface.

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“…We therefore decided to use a card-based UI (see Section V) that clusters the data into groups of data, and that sorts them with descending sensitivity to give a clear overview on the different data types, and to draw attention to the most sensitive data items first. As there is hardly any information about data types, clusters and sensitivity orders for intelligent retail data so far, we conducted two experiments prior to the main user study based on the results by Raber et al [15].…”

Section: A Background Analysismentioning

confidence: 99%

The 'Retailio' Privacy Wizard: Assisting Users with Privacy Settings for Intelligent Retail Stores

Raber¹,

Ziemann²,

Krueger³

2018

Proceedings 3rd European Workshop on Usable Security

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Intelligent retail stores like Amazon Go collect and process a large amount of shoppers' personal data to offer their service. In this paper we present Retailio, privacy management software that allows the customer to select the private data that should be accessible by retail stores. A privacy wizard helps the user to set her privacy settings, by using either a small informal privacy questionnaire or privacy measures extracted out of the user's Facebook posts for a machine learning-based prediction of user-tailored privacy settings. We conducted an expert interview to determine the different types of data that could be recorded in intelligent retail stores, and performed a user study to find out whether their disclosures correlate with shoppers' personalities. Retailio was evaluated in a validation study, regarding accuracy of the privacy wizard and user experience of the software. Our results show that there is a strong correlation between the IUIPC questionnaire and the data disclosure choice, which allowed us to predict the privacy settings with 70% accuracy.

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URetail: Privacy User Interfaces for Intelligent Retail Stores

Cited by 4 publications

References 6 publications

Development of an algorithm to protect user communication devices against data leaks

Development of an algorithm to protect user communication devices against data leaks

OmniWedges: Improved Radar-Based Audience Selection for Social Networks

The 'Retailio' Privacy Wizard: Assisting Users with Privacy Settings for Intelligent Retail Stores

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