An article, such as a news article or piece of investigative report. Newspapers and magazines have articles of many different types and this is intended to cover them all.
Defines user defined classes/types in this graph
The most generic kind of creative work, including books, movies, photographs, software programs, etc.
Provides basic data types such as integers, strings, etc. See https://www.w3.org/TR/rdf12-concepts/#section-Datatypes for additional datatypes that could be possible
Towards publishing my notes and research as self-describing, public information.
An organization such as a school, NGO, corporation, club, etc.
A person (alive, dead, undead, or fictional).
A file containing slides or used for a presentation.
Defines user defined properties in this graph
The simplest concept which all classes inherit from.
https://schema.org/CollegeOrUniversity http://dbpedia.org/resource/University
Notes on the paper "Zero-Knowledge Location Privacy via Accurate Floating-Point SNARKs"
Zero-Knowledge Location Privacy via Accurate Floating-Point SNARKs
An organization that the person is an alumni of.
Digital Object Identifier (DOI) for a resource.
A more useful version of rdfs:domain as it can be one of multiple values
We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only 64 constraints per operation for 2 15 single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has 15.9× less constraints utilizing single precision floating-point values, and 12.2× less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing — Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a setting, Bob can create a proof of (non-)proximity in 0.26 s, whereas Alice can verify her distance to about 470 peers per second.
A resource for anyone interested in understanding and unlocking the potential of zk-SNARKs, from beginners to experts.
Used to connect a class with its parent class