Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Saroja Erabelli

dblp:329/5803 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-6003-7134ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021Theory of computation · 1 · 1 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
Privacy and data protection · 48% Cryptographic protocols and secure computation · 45% Cryptographic primitives and cryptanalysis · 6%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection
differential privacy
1.012026
Shuffling Is Universal: Statistical Additive Randomized Encodings for All Functions · STOC 2026
Privacy and data protection › differential privacy › distributed differential privacy
shuffle model
1.012026
Shuffling Is Universal: Statistical Additive Randomized Encodings for All Functions · STOC 2026
Cryptographic protocols and secure computation › secure computation primitives
additive randomized encoding
0.912025
Additive Randomized Encodings from Public Key Encryption · CRYPTO (4) 2025
Cryptographic primitives and cryptanalysis › public-key cryptography
public-key encryption
0.312025
Additive Randomized Encodings from Public Key Encryption · CRYPTO (4) 2025

Methods — techniques the papers use, named apart from their topics

statistical additive randomized encoding · 1.0public-key encryption · 0.9
YearPublicationVenuePosition
2026 Shuffling Is Universal: Statistical Additive Randomized Encodings for All Functions
abstract
The shuffle model is a widely used abstraction for non-interactive anonymous communication. It allows n parties holding private inputs x1,…,xn to simultaneously send messages to an evaluator, so that the messages are received in a random order. The evaluator can then compute a joint function f(x1,…,xn), ideally while learning nothing else about the private inputs. The model has become increasingly popular both in cryptography, as an alternative to non-interactive secure computation in trusted setup models, and even more so in differential privacy, as an intermediate between the high-privacy, little-utility local model and the little-privacy, high-utility central curator model.
Nir Bitansky, Saroja Erabelli, Rachit Garg 0001, Yuval Ishai
STOC2
2025 Additive Randomized Encodings from Public Key Encryption
Nir Bitansky, Saroja Erabelli, Rachit Garg 0001
CRYPTO (4)2