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PrivacyScore: Improving Privacy and Security via Crowd-Sourced Benchmarks of Websites

Maass, Max ; Wichmann, Pascal ; Pridöhl, Henning ; Herrmann, Dominik (2021)
PrivacyScore: Improving Privacy and Security via Crowd-Sourced Benchmarks of Websites.
5th Annual Privacy Forum (APF 2017). Wien (07.-08.06.2017)
doi: 10.26083/tuprints-00017854
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: PrivacyScore: Improving Privacy and Security via Crowd-Sourced Benchmarks of Websites
Language: English
Date: 2021
Place of Publication: Cham
Year of primary publication: 2017
Publisher: Springer
Book Title: Privacy Technologies and Policy
Series: Lecture Notes in Computer Science
Series Volume: 10518
Event Title: 5th Annual Privacy Forum (APF 2017)
Event Location: Wien
Event Dates: 07.-08.06.2017
DOI: 10.26083/tuprints-00017854
Corresponding Links:
Origin: Secondary publication service

Website owners make conscious and unconscious decisions that affect their users, potentially exposing them to privacy and security risks in the process. In this paper we introduce PrivacyScore, an automated website scanning portal that allows anyone to benchmark security and privacy features of multiple websites. In contrast to existing projects, the checks implemented in PrivacyScore cover a wider range of potential privacy and security issues. Furthermore, users can control the ranking and analysis methodology. Therefore, PrivacyScore can also be used by data protection authorities to perform regularly scheduled compliance checks. In the long term we hope that the transparency resulting from the published assessments creates an incentive for website owners to improve their sites. The public availability of a first version of PrivacyScore was announced at the ENISA Annual Privacy Forum in June 2017.

Status: Postprint
URN: urn:nbn:de:tuda-tuprints-178548
Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Sichere Mobile Netze
DFG-Graduiertenkollegs > Research Training Group 2050 Privacy and Trust for Mobile Users
Date Deposited: 07 Apr 2021 07:28
Last Modified: 24 Nov 2022 14:44
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17854
PPN: 478852428
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