Demo Data from EDBT 2026 / reviewers in the wild / expert
Lior Jacob Strahilevitz
dblp:138/3938
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0003-8132-0056ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
3 papers |
Privacy and data protection · 95% Usable security · 5% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
privacy compliance |
1.0 | 2 | 2025 | Measuring Compliance with the California Consumer Privacy Act Over Space and Time · CHI 2024 Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA) · CHI 2025 |
Collaborative and social computing › crowdsourcing
crowd work |
0.4 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Privacy and data protection › privacy analysis › privacy models
contextual integrity |
0.4 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Usable security
privacy control |
0.1 | 1 | 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private Information · CHI 2020 |
Methods — techniques the papers use, named apart from their topics
web measurement · 0.9compliance analysis · 0.9survey · 0.9human-subjects experiment · 0.9longitudinal web measurement · 0.8automated tracking · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA)abstractTo protect consumer privacy, the California Consumer Privacy Act (CCPA) mandates that businesses provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy over the opt-out process allows them to impose hurdles on consumers aiming to opt out, including by employing dark patterns. Motivated by the enactment of the California Privacy Rights Act (CPRA), which strengthens the CCPA and explicitly forbids certain dark patterns in the opt-out process, we investigate how dark patterns are used in opt-out processes and assess their compliance with CCPA regulations. Our research reveals that websites employ a variety of dark patterns. Some of these patterns are explicitly prohibited under the CCPA; others evidently take advantage of legal loopholes. Despite the initial efforts to restrict dark patterns by policymakers, there is more work to be done. Van Hong Tran, Aarushi Mehrotra, Ranya Sharma, Marshini Chetty, Nick Feamster, Jens Frankenreiter, Lior Jacob Strahilevitz |
CHI | 7 |
| 2024 | Measuring Compliance with the California Consumer Privacy Act Over Space and TimeabstractThe widespread sharing of consumers’ personal information with third parties raises significant privacy concerns. The California Consumer Privacy Act (CCPA) mandates that online businesses offer consumers the option to opt out of the sale and sharing of personal information. Our study automatically tracks the presence of the opt-out link longitudinally across multiple states after the California Privacy Rights Act (CPRA) went into effect. We categorize websites based on whether they are subject to CCPA and investigate cases of potential non-compliance. We find a number of websites that implement the opt-out link early and across all examined states but also find a significant number of CCPA-subject websites that fail to offer any opt-out methods even when CCPA is in effect. Our findings can shed light on how websites are reacting to the CCPA and identify potential gaps in compliance and opt-out method designs that hinder consumers from exercising CCPA opt-out rights. Van Hong Tran, Aarushi Mehrotra, Marshini Chetty, Nick Feamster, Jens Frankenreiter, Lior Jacob Strahilevitz |
CHI | 6 |
| 2020 | Taking Data Out of Context to Hyper-Personalize Ads: Crowdworkers' Privacy Perceptions and Decisions to Disclose Private InformationabstractData brokers and advertisers increasingly collect data in one context and use it in another. When users encounter a misuse of their data, do they subsequently disclose less information? We report on human-subjects experiments with 25 in-person and 280 online participants. First, participants provided personal information amidst distractor questions. A week later, while participants completed another survey, they received either a robotext or online banner ad seemingly unrelated to the study. Half of the participants received an ad containing their name, partner's name, preferred cuisine, and location; others received a generic ad. We measured how many of 43 potentially invasive questions participants subsequently chose to answer. Participants reacted negatively to the personalized ad, yet answered nearly all invasive questions accurately. We unpack our results relative to the privacy paradox, contextual integrity, and power dynamics in crowdworker platforms. Julia Hanson, Miranda Wei, Sophie Veys, Matthew Kugler, Lior Jacob Strahilevitz, Blase Ur |
CHI | 5 |