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Private Collection Matching Protocols

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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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Kasra EdalatNejad, Mathilde Raynal, Wouter Lueks, Carmela Troncoso

Cryptography and Security · Submitted 14 Jun 2022

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

The authors' main claim Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost.

how it is stated · asserted 1.00 read the record →

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arXiv ID2206.07009 (version 2)
Pith IDQQM4SWBO, Pith's permanent record of this paper and its review
CitationCarmela Troncoso, Kasra EdalatNejad, Mathilde Raynal, Wouter Lueks. "Private Collection Matching Protocols." arXiv:2206.07009 (2022). https://pith.science/paper/QQM4SWBO
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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-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,884 input tokens.

What the abstract itself states, as probabilities: claims first 0.17 · states limitation 0.33 · human participants 0.04 · controlled comparison 0.28 · randomized assignment 0.01 · states quantitative result 0.49 · compares with prior results 0.91 · mentions code or data availability 0.07.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. Existing privacy-preserving cryptographic primitives cannot solve PCM problems efficiently without harming privacy. We propose a modular framework that enables designers to build privacy-preserving PCM systems that output one bit: whether a collection of server sets matches the client's set. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server elements. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PCM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost.

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 We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. contribution 0.58 0.69 0.07 asserted 0.99
s1 Existing privacy-preserving cryptographic primitives cannot solve PCM problems efficiently without harming privacy. gap 0.97 0.11 0.08 asserted 1.00
s2 We propose a modular framework that enables designers to build privacy-preserving PCM systems that output one bit: whether a collection of server sets matches the client's set. contribution 0.94 0.93 0.31 asserted 0.88
s3 The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server elements. result 0.84 0.88 0.12 asserted 1.00
s4 We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PCM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. contribution 0.98 0.95 0.11 asserted 0.94
s5 Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost. result 0.92 0.94 0.57 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.53.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • 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

We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. Existing privacy-preserving cryptographic primitives cannot solve PCM problems efficiently without harming privacy. We propose a modular framework that enables designers to build privacy-preserving PCM systems that output one bit: whether a collection of server sets matches the client's set. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server elements. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PCM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost.

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