Demo Data from SIGIR 2026 / reviewers in the wild / expert
Aaron Eline
dblp:317/0570
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2024
0000-0002-9105-4922ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 3 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
2 papers |
Authentication and access control · 57% Systems and software security · 43% | |
| Software engineering, system software, and programming languages
3 papers |
Programming languages and type systems · 50% Program verification · 42% Program analysis · 8% |
Topics — the 12 heaviest of 14, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Authentication and access control
access control |
0.8 | 1 | 2024 | Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization · Proc. ACM Program. Lang. 2024 |
Authentication and access control › access control models
attribute-based access control |
0.8 | 1 | 2024 | Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization · Proc. ACM Program. Lang. 2024 |
Authentication and access control
authorization |
0.8 | 1 | 2024 | Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization · Proc. ACM Program. Lang. 2024 |
Authentication and access control › access control
role-based access control |
0.8 | 1 | 2024 | Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization · Proc. ACM Program. Lang. 2024 |
Systems and software security › memory safety
buffer overflow |
0.6 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Systems and software security
memory safety |
0.6 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Systems and software security › memory safety
spatial memory safety |
0.6 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Systems and software security
vulnerability discovery |
0.6 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Program verification
proof assistants |
0.6 | 1 | 2022 | Computing correctly with inductive relations · PLDI 2022 |
Programming languages and type systems › metaprogramming
reflection |
0.6 | 1 | 2022 | Computing correctly with inductive relations · PLDI 2022 |
Program verification
annotation inference |
0.2 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Program analysis
static analysis |
0.2 | 1 | 2022 | C to checked C by 3c · Proc. ACM Program. Lang. 2022 |
Methods — techniques the papers use, named apart from their topics
proof assistant · 1.5logical encoding · 1.5static analysis · 1.1root cause analysis · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Cedar: A New Language for Expressive, Fast, Safe, and Analyzable AuthorizationabstractCedar is a new authorization policy language designed to be ergonomic, fast, safe, and analyzable. Rather than embed authorization logic in an application’s code, developers can write that logic as Cedar policies and delegate access decisions to Cedar’s evaluation engine. Cedar’s simple and intuitive syntax supports common authorization use-cases with readable policies, naturally leveraging concepts from role-based, attribute-based, and relation-based access control models. Cedar’s policy structure enables access requests to be decided quickly. Cedar’s policy validator leverages optional typing to help policy writers avoid mistakes, but not get in their way. Cedar’s design has been finely balanced to allow for a sound and complete logical encoding, which enables precise policy analysis, e.g., to ensure that when refactoring a set of policies, the authorized permissions do not change. We have modeled Cedar in the Lean programming language, and used Lean’s proof assistant to prove important properties of Cedar’s design. We have implemented Cedar in Rust, and released it open-source. Comparing Cedar to two open-source languages, OpenFGA and Rego, we find (subjectively) that Cedar has equally or more readable policies, but (objectively) performs far better. Joseph W. Cutler, Craig Disselkoen, Aaron Eline, Shaobo He 0002, Kyle Headley, Michael Hicks 0001, Kesha Hietala, Eleftherios Ioannidis, John H. Kastner, Anwar Mamat, Darin McAdams, Matt McCutchen, Neha Rungta, Emina Torlak, Andrew Wells |
Proc. ACM Program. Lang. | 3 |
| 2022 | Computing correctly with inductive relationsabstractInductive relations are the predominant way of writing specifications in mechanized proof developments. Compared to purely functional specifications, they enjoy increased expressive power and facilitate more compositional reasoning. However, inductive relations also come with a significant drawback: they can’t be used for computation. Zoe Paraskevopoulou, Aaron Eline, Leonidas Lampropoulos |
PLDI | 2 |
| 2022 | C to checked C by 3cabstractOwing to the continued use of C (and C++), spatial safety violations (e.g., buffer overflows) still constitute one of today's most dangerous and prevalent security vulnerabilities. To combat these violations, Checked C extends C with bounds-enforced checked pointer types. Checked C is essentially a gradually typed spatially safe C - checked pointers are backwards-binary compatible with legacy pointers, and the language allows them to be added piecemeal, rather than necessarily all at once, so that safety retrofitting can be incremental. This paper presents a semi-automated process for porting a legacy C program to Checked C. The process centers on 3C, a static analysis-based annotation tool. 3C employs two novel static analysis algorithms - typ3c and boun3c - to annotate legacy pointers as checked pointers, and to infer array bounds annotations for pointers that need them. 3C performs a root cause analysis to direct a human developer to code that should be refactored; once done, 3C can be re-run to infer further annotations (and updated root causes). Experiments on 11 programs totaling 319KLoC show 3C to be effective at inferring checked pointer types, and experience with previously and newly ported code finds 3C works well when combined with human-driven refactoring. Aravind Machiry, John H. Kastner, Matt McCutchen, Aaron Eline, Kyle Headley, Michael Hicks 0001 |
Proc. ACM Program. Lang. | 4 |