Month: November 2021

The best paper title I’ve ever had: Thirdeye

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So as I get more into biometrics I have to think of cool paper titles.  This is a cool work on iris recognition that uses the triplet loss to train the underlying neural network.  We managed to think of the name ThirdEye which is just about perfect.  Iris recognition is usually four steps:

  1. Segmentation: split pixels into iris/non-iris,
  2. Normalization: split the variable size image into a fixed dimension,
  3. Feature extraction: remove noise and further reduce dimension, and
  4. Comparison: compare the resulting template with a previous version.

Using the triplet loss with modern neural networks we challenge the belief that normalization is helpful (at least in all situations).  Source is here: https://github.com/sohaib50k/ThirdEye—Iris-recognition-using-triplets

 

We were inspired by a similar paper called “Influence of Segmentation on deep iris recognition performance” by Lozej, Stepec, Struc, and Peer that asked if segmentation was useful.

8 Submissions to publication!!! Code Offset in the Exponent

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This paper is finally published.  Its my new record for number of needed submissions.  The worst part about it is that it was Luke’s first paper and this unnecessarily stunted his growth.

 

I think its very cool but I say that about everything I end up writing up!

Abstract: Fuzzy extractors transform a noisy source e into a stable key which can be reproduced from a nearby value e’. They are a fundamental tool for key derivation from biometric sources. This work introduces code offset in the exponent and uses this construction to build the first reusable fuzzy extractor that simultaneously supports structured, low entropy distributions with correlated symbols and confidence information. These properties are specifically motivated by the most pertinent applications—key derivation from biometrics and physical unclonable functions—which typically demonstrate low entropy with additional statistical correlations and benefit from extractors that can leverage confidence information for efficiency. Code offset in the exponent is a group encoding of the code offset construction (Juels and Wattenberg, CCS 1999) that stores the value e in a one-time pad which is sampled as a codeword, Ax, of a linear error-correcting code: Ax+ e. Rather than encoding Ax+ e directly, code offset in the exponent calls for encoding by exponentiation of a generator in a cryptographically strong group. We demonstrate security of the construction in the generic group model, establishing security whenever the inner product between the error distribution and all vectors in the null space of the code is unpredictable. We show this condition includes distributions supported by multiple prior fuzzy extractors. Our analysis also shows a prior construction of pattern matching obfuscation (Bishop et al., Crypto 2018) is secure for more distributions than previously known.

My pretenure path at UConn/tenure packet submitted!

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It is been a long time since I’ve posted something here, roughly corresponding with the start of the COVID pandemic.  During that time I didn’t travel as much and it was easy to not think about the value of communicating with the outside.  In Fall 2019, I tore my quad (and broke my kneecap and toe) and this put me in less of a communicative mood.  We also found out in Spring 2020 that my wife was pregnant.  Delivering our first child, Ayla Helen Fuller on September 24, 2020.

We were trying to be first time parents at home while preparing for tenure application.  Honestly, the COVID pandemic was really hard on research productivity.  It was particularly hard for theory students to make progress without in person white board sessions.  It seemed that everyone had much more to do without making much progress.

I had a streak of about 10 straight rejections including a record of 8 submissions for a paper before acceptance.  Coupled with several important grant rejections/cancellations this left the lab in a rough place.  Thanks to some help from UConn we’ve managed to keep supporting students.  It is been a tough two years.

I’m writing because in academia (and all aspects of life) we see others success and tend to see people that are vastly more successful/capable than we are and compare to them.  Everyone sees the professor/Ph.D. student that is publishing multiple times every year at the top conference.  As they say “Comparison is the thief of joy.”

I’m writing this post because that I’ve submitted my tenure packet to UConn and from here on out (assuming I don’t lose my job) any comparison that I do will be purely internal.  I’m writing this post in part to remind myself that all comparisons are my doing and they never make me feel good.  I’m sure there’s some people reading this who view me as a competent member of a community, some who don’t know who am I, and some who aspire to the level of success I have achieved.

All I know is that I love teaching, mentoring students, discovering new results, and communicating science to others.  I don’t know how good I am at any of these things.  I’d like to say that I don’t care but I do deeply.  Maybe that will change someday as external pressures reduce.  But for now I’m looking back through my CV and seen I’ve:

  • Co-authored papers with 56 people,
  • Supervised 8 graduate students and learn a ton from all of them,
  • Supervised 23 undergraduates in research in five years at UConn (including 8 honors thesis), and
  • Built four new courses (Crypto II, Network Security, Computer Security, and our Cybersecurity Lab)

I’m going to try and measure my success in terms of positive impact made on those around me.  I am thrilled to work with wonderful people and I hope I can help them achieve their dreams.  I’m not the best in this field, I’m not the worst (either as a person or a researcher).  I believe I’m helping the world and those around me.

Lab Meeting