Feb. 13, 2024, 5:45 a.m. | Alessandro Epasto Vahab Mirrokni Bryan Perozzi Anton Tsitsulin Peilin Zhong

cs.LG updates on arXiv.org arxiv.org

Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most important recent concerns, existing PPR algorithms are not designed to protect user privacy. PPR is highly sensitive to the input graph edges: the difference of only one edge may cause a big change in the PPR vector, potentially leaking private user data.
In this work, we propose an algorithm which …

algorithms concerns cs.cr cs.lg cs.si data data privacy embedding graph graph learning labeling node pagerank personalized privacy protect ranking sensitivity stat.ml tool unsupervised unsupervised learning via

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Engineering Director-Big Data technologies (Hadoop, Spark, Hive, Kafka)

@ Visa | Bengaluru, India

Senior Data Engineer

@ Manulife | Makati City, Manulife Philippines Head Office

GDS Consulting Senior Data Scientist 2

@ EY | Taguig, PH, 1634

IT Data Analyst Team Lead

@ Rosecrance | Rockford, Illinois, United States