From WHOIS to WHOWAS: A Large-Scale Measurement Study of Domain Registration Privacy under the GDPR

Lü, C.; Liu, Baojun; Zhang, Yiming; Zhou, Li; Fenglu, Zhang; Duan, Haixin; Liu, Ying; Chen, Joann Qiongna; Liang, Jie; Zhang, Zaifeng; Hao, Shuang; Yang, Min

doi:10.14722/ndss.2021.23134

Cited by 20 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study found that 85% of registries and registrars had censored WHOIS information. The authors also observed that approximately 60% applied ICANN's temporary speci cation outside the European Economic Area [13].…”

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

WHOIS Data Redaction and its Impact on Unsolicited Emails: A Field Experiment

Sattler

2023

Preprint

View full text Add to dashboard Cite

The Internet Corporation for Assigned Names and Numbers (ICANN) mandates the disclosure of certain information for registered domain names. However, the European Union General Data Protection Regulation (GDPR) curtails what information can be publicly accessible, including personally identifiable information (PII). It has been suggested that public disclosure of registration data leads to email spam, but no systematic study of this effect has been conducted. This study fills this gap by empirically assessing the impact of public PII disclosure on spam email volume. Over a year-long field experiment, we registered 66 domain names with disclosed and undisclosed PII and analyzed incoming unsolicited email advertising. Our results revealed that domains with publicly disclosed contact information received a mean of 19.7 spam emails per domain, more than quadrupling the mean of 4.2 for domains with undisclosed information. This significant increase in spam emails linked to public PII disclosure indicates a clear negative impact. ICANN stakeholders should consider these findings, recognizing the potential privacy implications and spam risks of disclosing domain contact information.

show abstract

Section: Introductionmentioning

confidence: 95%

“…Published domain registration data is used for information technology security analyses, website certi cates, and email marketing [12,13]. Paradoxically, the publication of this data is considered one of the sources of email spam [13][14][15][16][17], underlining the complex implications of publicly disclosing domain registration data.…”

Section: Introductionmentioning

confidence: 99%

WHOIS Data Redaction and its Impact on Unsolicited Emails: A Field Experiment

Sattler

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the lead up to the enforcement of the GDPR in 2018, EU data protection regulators, in their capacity under the Article 29 Working Party (now re-constituted as the European Data Protection Board), instructed ICANN (Internet Corporation for Assigned Names and Numbers, the organisation responsible for co-ordinating DNS) to make such WHOIS information private by default. While ICANN have been attempting to develop an alternative access system for years, which would protect privacy whilst also making WHOIS information available to those with a lawful basis for processing it, the implementation of such tiered access systems by individual domain name registrars has been uneven [65]. These factors make it hard to compile tracker lists based on WHOIS data.…”

Section: Network Traffic Analysismentioning

confidence: 99%

Tracking on the Web, Mobile and the Internet-of-Things

Binns¹

2022

Preprint

View full text Add to dashboard Cite

Tracking' is the collection of data about an individual's activity across multiple distinct contexts and the retention, use, or sharing of data derived from that activity outside the context in which it occurred. This paper aims to introduce tracking on the web, smartphones, and the Internet of Things, to an audience with little or no previous knowledge. It covers these topics primarily from the perspective of computer science and humancomputer interaction, but also includes relevant law and policy aspects. Rather than a systematic literature review, it aims to provide an overarching narrative spanning this large research space.Section 1 introduces the concept of tracking. Section 2 provides a short history of the major developments of tracking on the web. Section 3 presents research covering the detection, measurement and analysis of web tracking technologies. Section 4 delves into the countermeasures against web tracking and mechanisms that have been proposed to allow users to control and limit tracking, as well as studies into end-user perspectives on tracking. Section 5 focuses on tracking on 'smart' devices including smartphones and the internet of things. Section 6 covers emerging issues affecting the future of tracking across these different platforms.42 https://perma.cc/UZ7Y-EHLZ 43 https://perma.cc/XL8L-HZJU

show abstract

“…User Recognition. It seems easier to find the owners of devices, using public databases such as Whois [4,5] and DNS [6,7]. But these methods can only get the register of websites.…”

Section: Related Workmentioning

confidence: 99%

Understanding User-Level IP Blocks on the Internet

Ren

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Generally, the devices on the Internet are identified by IP addresses. The users of IPs are those who use IPs on the Internet and are always different from their registers and operators. Since IPs are used as unique identifiers of devices, knowing users of IPs according to their multisource data is critical for experts to protect the security of the network. At present, there are only few methods to mine the users of IPs from their public data. To make matters worse, the existing methods do not make effective use of a large amount and multisource data, such as certificates, protocol banner, rDNS, location, topology, etc. As a result, the performances of existing methods are largely limited. To tackle this issue, we proposed ULIB, short for “Understanding User-Level IP Blocks on the Internet.” ULIB is based on improved community detection to mine the users for as many blocks of IPs as possible. By analysing comprehensive attributes of IPs, ULIB is able to recognize users effectively. Meanwhile, we evaluated our methodology in the real world and the experiments demonstrated that the accuracy of ULIB is 74.20% and the coverage is 28.90% in a city of China, which outperforms other existing methods.

show abstract

From WHOIS to WHOWAS: A Large-Scale Measurement Study of Domain Registration Privacy under the GDPR

Cited by 20 publications

References 56 publications

WHOIS Data Redaction and its Impact on Unsolicited Emails: A Field Experiment

WHOIS Data Redaction and its Impact on Unsolicited Emails: A Field Experiment

Tracking on the Web, Mobile and the Internet-of-Things

Understanding User-Level IP Blocks on the Internet

Contact Info

Product

Resources

About