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Featherweight PINQ

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Hamid Ebadi, David Sands

Programming Languages · Submitted 11 May 2015

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

The authors' main claim We prove that any program interacting with featherweight PINQ's API is differentially private.

how it is stated · asserted 1.00 read the record →

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arXiv ID1505.02642 (version 1)
Pith IDM4B6BK6I, Pith's permanent record of this paper and its review
CitationDavid Sands, Hamid Ebadi. "Featherweight PINQ." arXiv:1505.02642 (2015). https://pith.science/paper/M4B6BK6I
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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, 4,161 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.08 · human participants 0.05 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.25 · mentions code or data availability 0.04.

The abstract, sentence by sentence

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

Differentially private mechanisms enjoy a variety of composition properties. Leveraging these, McSherry introduced PINQ (SIGMOD 2009), a system empowering non-experts to construct new differentially private analyses. PINQ is an LINQ-like API which provides automatic privacy guarantees for all programs which use it to mediate sensitive data manipulation. In this work we introduce featherweight PINQ, a formal model capturing the essence of PINQ. We prove that any program interacting with featherweight PINQ's API is differentially private.

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 Differentially private mechanisms enjoy a variety of composition properties. background 1.00 0.06 0.02 asserted 1.00
s1 Leveraging these, McSherry introduced PINQ (SIGMOD 2009), a system empowering non-experts to construct new differentially private analyses. background 1.00 0.04 0.04 asserted 1.00
s2 PINQ is an LINQ-like API which provides automatic privacy guarantees for all programs which use it to mediate sensitive data manipulation. background 0.99 0.07 0.05 asserted 1.00
s3 In this work we introduce featherweight PINQ, a formal model capturing the essence of PINQ. contribution 0.99 0.94 0.07 asserted 1.00
s4 We prove that any program interacting with featherweight PINQ's API is differentially private. result 0.92 0.95 0.88 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.82 · evidence from proof or derivation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
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

Differentially private mechanisms enjoy a variety of composition properties. Leveraging these, McSherry introduced PINQ (SIGMOD 2009), a system empowering non-experts to construct new differentially private analyses. PINQ is an LINQ-like API which provides automatic privacy guarantees for all programs which use it to mediate sensitive data manipulation. In this work we introduce featherweight PINQ, a formal model capturing the essence of PINQ. We prove that any program interacting with featherweight PINQ's API is differentially private.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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