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IFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm

arXiv 2205.09534 ↗First posted 19 May 2022cs.HC cs.AI cs.LGcited by 0

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Abstract (arXiv)

IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a color respectively. To communicate their digit, users press the button with the same color that is assigned to their digit, which can thus be identified by elimination after a few iterations. IFTT-PIN uses the same principle but does not pre-assign colors to each button. Instead, users are free to choose which button to use for each color. The button-to-color mapping only exists in the user's mind and is never directly communicated to the interface. In other words, IFTT-PIN infers both the user's PIN and their preferred button-to-color mapping at the same time, a process called self-calibration. In this paper, we present online interactive demonstrations of IFTT-PIN with and without self-calibration and introduce the key concepts and assumptions making self-calibration possible. IFTT-PIN can be tested at https://jgrizou.github.io/IFTT-PIN/ with a video introduction available at https://youtu.be/5I1ibPJdLHM. We review related work in the field of brain-computer interface and further propose self-calibration as a novel approach to protect users against shoulder surfing attacks. Finally, we introduce a vault cracking challenge as a test of usability and security that was informally tested at our institute. With IFTT-PIN, we wish to demonstrate a new interactive experience where users can decide actively and on-the-fly how to use an interface. The self-calibration paradigm might lead to novel opportunities for interaction in other applications or domains. We hope this work will inspire the community to invent them.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 43 references that are in the map. First posted on arXiv 19 May 2022.

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References · 3

  1. 2014A Plug&Play P300 BCI Using Information Geometrycited by 14
  2. 2017Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guaranteescited by 9
  3. 2019Interactively shaping robot behaviour with unlabeled human instructionscited by 8

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