IEEE Conference on Secure and Trustworthy Machine Learning
February 8-10, 2023
Hilton Raleigh North Hills
Raleigh, North Carolina
https://satml.org/

We solicit research papers, systematization of knowledge papers, and
position papers (see below for details about each of these
categories).

Areas of Interest include (but are not limited to):

    Trustworthy data curation
    Novel attacks on ML systems
    Methods for defending against attacks on ML systems
    Forensic analysis of ML systems
    Verifying properties of ML systems
    Securely and safely integrating ML into systems
    Privacy (e.g., confidentiality, inference privacy, machine unlearning)
    Fairness
    Accountability
    Transparency
    Interpretability

Important Dates

    Abstracts due for Paper​ ​​submissions: Monday, August 22, 2022
    (11:59 PM AoE, UTC-12)
    Paper​ ​submission: Thursday, September 1, 2022 (11:59 PM AoE, UTC-12)
    Paper​ ​notification: Tuesday, November 15, 2022
    Camera-ready​ ​versions​ of Papers and Abstracts: Monday, December 15, 2022
    Conference: Wednesday, February 8​ ​to Friday,​ February​ ​10,​ ​2023

Submission Details

    The website for submissions is
    https://openreview.net/group?id=IEEE.org/SaTML/2023/Conference

    Submissions must use the two-column IEEE Proceedings style:
    https://www.ieee.org/conferences/publishing/templates.html (Note:
    use \documentclass[conference]{IEEEtran} for your submission)

Submission Categories

Research Papers, up to 12 pages of body text, with unlimited
additional space for references and well-marked appendices. These must
be well-argued and worthy of publication and​ ​citation,​ ​on​ ​the​ ​topics​
above.​ ​Research​ ​papers​ ​must​ ​present​ ​new​ ​work​, evidence, ​or​ ​ideas.

Systematization of Knowledge papers, up to 12 pages of body text,
should ​provide​ ​an integration​ ​and​ ​clarification​ ​of​ ​ideas​ ​on​ ​an​
established,​ ​major​ ​research​ ​area,​ ​support​ ​or challenge​ ​long-held​
beliefs​ ​in​ ​such​ ​an​ ​area​ ​with​ ​compelling​ ​evidence,​ ​or​ ​present​ ​a
convincing,​ ​comprehensive​ ​new​ ​taxonomy​ ​of​ ​some​ ​aspect​ ​of​ secure and
trustworthy machine learning. When submitting a systematization of
knowledge paper, prepend "SoK:" to the title of your paper on
OpenReview.

Position​ ​papers​ ​with​ ​novel visions, with a minimum of 5 pages of body
text, ​will​ ​also​ ​be​ ​considered.​ Reviewers will be asked to evaluate
vision as bringing opinions and views that pertain to issues of broad
interest to the computing community, typically, but not exclusively,
of a nontechnical nature. Controversial issues will not be avoided but
be dealt with fairly. Authors are welcome to submit carefully reasoned
"Viewpoints" in which positions are substantiated by facts or
principled arguments. Vision may relate to the wide and abundant
spectrum of the computing field of trustworthy machine learning—its
open challenges, technical visions and perspectives, educational
aspects, societal impact, significant applications and research
results of high significance and broad interest. Position papers
should set the background and provide introductory references, define
fundamental concepts, compare alternate approaches, and explain the
significance or application of a particular technology or result by
means of well-reasoned text and pertinent graphical material. The use
of sidebars to illustrate significant points is encouraged. When
submitting a position paper, prepend "Position:" to the title of your
paper on OpenReview.  Review Information

While a paper is under submission to this conference, authors may
choose to give talks about their work, post a preprint of the paper
online, and disclose security vulnerabilities to vendors.

To improve the fairness of the reviewing process, SaTML will follow a
double-blind reviewing process. Submitted papers must (a) omit any
reference to the authors' names or the names of their institutions,
and (b) reference the authors' own related work in the third person
(e.g., not "We build on our previous work …" but rather "We build on
the work of ..."). Nothing should be done in the name of anonymity
that weakens the submission or makes the job of reviewing the paper
more difficult (e.g., important background references should not be
omitted or anonymized). Please see this double-blind FAQ for the
answers to many common concerns about SaTML's double-blind reviewing
process. When in doubt, contact the program chairs. We are devoted to
seeking broad representation in the program, and may take this into
account when reviewing multiple submissions from the same author.
Reviews from Prior Submissions

For papers that were previously submitted to, and rejected from,
another conference, authors are required to append to their submission
PDF the (anonymized, but otherwise unedited) prior reviews along with
a description of how those reviews were addressed in the current
version of the paper. Authors are only required to include reviews
from the last time the paper was submitted. Authors who try to
circumvent this rule (e.g., by changing the title of the paper without
significantly changing the contents) may have their papers rejected
without further consideration, at the discretion of the PC chairs.

Submissions must use the two-column IEEE Proceedings style:
https://www.ieee.org/conferences/publishing/templates.html (Note: use
\documentclass[conference]{IEEEtran} for your submission)

Failure to adhere to these rules is grounds for rejection.
Submission Decisions

For each submission, one of the following decisions will be made:

Accept: Papers in this category will be accepted for publication in
the proceedings and presentation at the conference, possibly after
making minor changes with the oversight of a shepherd (Minor
Revision). Within one month of acceptance, all accepted papers must
submit a camera-ready copy incorporating reviewer feedback. The papers
will immediately be published, open access, in the Computer Society's
Digital Library, and they may be cited as "To appear in the IEEE
Conference on Secure and Trustworthy Machine Learning, February 2023".

Major Revision: A limited number of papers will be invited to
submit a major revision; such papers will receive a detailed summary
of expectations for revision, in addition to standard reviewer
comments. Authors will have a limited time window to submit revision
after the notification is sent. The authors should clearly explain in
a well-marked appendix how the revisions address the comments of the
reviewers. The revised paper will then be re-evaluated, and either
accepted or rejected. We will assign the same set of
reviewers. Authors can choose to withdraw their paper and not submit a
revision.

Reject: Papers in this category are declined for inclusion in the
      conference.

Rebuttal Period

We will have a rebuttal period during which authors have the
opportunity to exchange messages with the reviewers, respond to
questions asked, and address reviewer comments in the paper. To this
end, we will use an anonymous communication feature to enable
communication between authors and reviewers. The authors should mainly
focus on factual errors in the reviews and concrete questions posed by
the reviewers. New research results can also be discussed if they help
to clarify open questions. More instructions will be sent out to the
authors at the beginning of the rebuttal period.

Best Paper Award

Outstanding paper(s) will be selected by the Program Committee, with
input from the Steering Committee, for the best paper award. The award
will be announced at the conference. Best paper awards are intended to
highlight papers which significantly challenge the state of the art in
research areas relevant to SaTML.  Attendance for authors of accepted
papers

At least one author​ ​of​ ​accepted papers​ ​will​ ​present​ ​their​ ​work​ ​at​ ​the​
conference​ ​and​ ​their papers​ ​will​ ​appear​ ​in​ ​the​ ​conference's​ ​formal​
IEEE​ ​proceedings. In the event of difficulty in obtaining visas for
travel and other exceptional circumstances, exceptions may be made and
will be discussed on a case-by-case basis.

If you have any questions, please email us at contact@satml.org