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Secure Friend Discovery via Privacy-Preserving and Decentralized Community Detection

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Pili Hu, Sherman S.M. Chow, Wing Cheong Lau

Cryptography and Security · Submitted 20 May 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Preliminary theoretical results show that our protocol can uncover communities with overwhelming probability and preserve privacy.

how it is stated · asserted 0.72 read the record →

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arXiv ID1405.4951 (version 1)
Pith IDKBJTCXP4, Pith's permanent record of this paper and its review
CitationPili Hu, Sherman S.M. Chow, Wing Cheong Lau. "Secure Friend Discovery via Privacy-Preserving and Decentralized Community Detection." arXiv:1405.4951 (2014). https://pith.science/paper/KBJTCXP4
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,389 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.96 · states limitation 0.24 · human participants 0.05 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.10 · compares with prior results 0.09 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery. We formulate the first privacy-preserving and decentralized community detection problem as a multi-objective optimization. We design the first protocol to solve this problem, which transforms community detection to a series of Private Set Intersection (PSI) instances using Truncated Random Walk (TRW). Preliminary theoretical results show that our protocol can uncover communities with overwhelming probability and preserve privacy. We also discuss future works, potential extensions and variations.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The problem of secure friend discovery on a social network has long been proposed and studied. background 1.00 0.03 0.02 asserted 1.00
s1 The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. background 1.00 0.04 0.02 asserted 0.99
s2 In this paper, we propose to use community detection to tackle the problem of secure friend discovery. contribution 0.84 0.80 0.08 asserted 0.85
s3 We formulate the first privacy-preserving and decentralized community detection problem as a multi-objective optimization. contribution 0.94 0.92 0.08 asserted 0.99
s4 We design the first protocol to solve this problem, which transforms community detection to a series of Private Set Intersection (PSI) instances using Truncated Random Walk (TRW). method 0.74 0.95 0.11 asserted 1.00
s5 Preliminary theoretical results show that our protocol can uncover communities with overwhelming probability and preserve privacy. result 0.99 0.91 0.62 asserted 0.72
s6 We also discuss future works, potential extensions and variations. future work 1.00 0.37 0.02 unclear 0.65

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.92.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery. We formulate the first privacy-preserving and decentralized community detection problem as a multi-objective optimization. We design the first protocol to solve this problem, which transforms community detection to a series of Private Set Intersection (PSI) instances using Truncated Random Walk (TRW). Preliminary theoretical results show that our protocol can uncover communities with overwhelming probability and preserve privacy. We also discuss future works, potential extensions and variations.

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