Demo Data from EDBT 2026 / reviewers in the wild / expert
Fengyan Han
dblp:86/10208
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-5761-2524ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 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
1 paper |
Authentication and access control · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Authentication and access control › mobile authentication
smartphone authentication |
0.7 | 1 | 2023 | Squeez'In: Private Authentication on Smartphones based on Squeezing Gestures · CHI 2023 |
Authentication and access control
privacy-preserving authentication |
0.2 | 1 | 2023 | Squeez'In: Private Authentication on Smartphones based on Squeezing Gestures · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
user study · 1.3gesture recognition · 1.3SVM · 1.3GBDT · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Squeez'In: Private Authentication on Smartphones based on Squeezing GesturesabstractIn this paper, we proposed Squeez’In, a technique on smartphones that enabled private authentication by holding and squeezing the phone with a unique pattern. We first explored the design space of practical squeezing gestures for authentication by analyzing the participants’ self-designed gestures and squeezing behavior. Results showed that varying-length gestures with two levels of touch pressure and duration were the most natural and unambiguous. We then implemented Squeez’In on an off-the-shelf capacitive sensing smartphone, and employed an SVM-GBDT model for recognizing gestures and user-specific behavioral patterns, achieving 99.3% accuracy and 0.93 F1-score when tested on 21 users. A following 14-day study validated the memorability and long-term stability of Squeez’In. During usability evaluation, compared with gesture and pin code, Squeez’In achieved significantly faster authentication speed and higher user preference in terms of privacy and security. Xin Yi 0001, Louisa Shi, Fengyan Han, Yan Kong, Hewu Li, Yuanchun Shi |
CHI | 5 |