Private Approximate Heavy Hitters
Martin J. Strauss, Xuan Zheng
Abstract
We consider the problem of private computation of approximate Heavy Hitters. Alice and Bob each hold a vector and, in the vector sum, they want to find the B largest values along with their indices. While the exact problem requires linear communication, protocols in the literature solve this problem approximately using polynomial computation time, polylogarithmic communication, and constantly many rounds. We show how to solve the problem privately with comparable cost, in the sense that nothing is learned by Alice and Bob beyond what is implied by their input, the ideal top-B output, and goodness of approximation (equivalently, the Euclidean norm of the vector sum). We give lower bounds showing that the Euclidean norm must leak by any efficient algorithm.
Create a lesson
Related papers
RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution
Junjie Zhang, Hui Liu, Kecheng Chen et al.
Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners
Qianlong Lan, Vinothini Pandurangan, Anuj Kaul et al.
When Context Gets Root: Privilege Escalation in LLM Harnesses
Xingbang He, Yuanwei Chen, Yi Qian et al.
Low-ASR Backdoors: Exploiting Attack Success Rate Reduction and Attacker-Defender Asymmetry
Arham Riaz, Ting Yu
SPA: Securing Persistent LLM Agents Across Queries with Plan-First Information-Flow Control
Dylan Girrens, Guangjing Wang
From Security Events to Conflict States: A Three-layer Cyber Defense Scenario Model for Enhanced Cyber Situational Awareness
Miguel Requena Micó, Mario Fernandez-Tarraga, Daniel Díaz-López et al.