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Differentially Private Online Item Pricing

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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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Joon Suk Huh

Computer Science and Game Theory · Submitted 19 May 2023

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

The authors' main claim Notably, our algorithm is the first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a privacy guarantee.

how it is stated · asserted 1.00 read the record →

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arXiv ID2305.11362 (version 3)
Pith IDJJFILHWW, Pith's permanent record of this paper and its review
CitationJoon Suk Huh. "Differentially Private Online Item Pricing." arXiv:2305.11362 (2023). https://pith.science/paper/JJFILHWW
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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, 5,466 input tokens.

What the abstract itself states, as probabilities: claims first 0.91 · states limitation 0.13 · human participants 0.05 · controlled comparison 0.05 · randomized assignment 0.04 · states quantitative result 0.97 · compares with prior results 0.70 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

This work addresses the problem of revenue maximization in a repeated, unlimited supply item-pricing auction while preserving buyer privacy. We present a novel algorithm that provides differential privacy with respect to the buyer's input pair: item selection and bid. Notably, our algorithm is the first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a privacy guarantee. Our method is based on an exponential weights meta-algorithm, and we mitigate the issue of discontinuities in revenue functions via small random perturbations. As a result of its structural similarity to the exponential mechanism, our method inherently secures differential privacy. We also extend our algorithm to accommodate scenarios where buyers strategically bid over successive rounds. The inherent differential privacy allows us to adapt our algorithm with minimal modification to ensure a sublinear regret in this setting.

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 This work addresses the problem of revenue maximization in a repeated, unlimited supply item-pricing auction while preserving buyer privacy. background 0.98 0.43 0.07 asserted 0.96
s1 We present a novel algorithm that provides differential privacy with respect to the buyer's input pair: item selection and bid. contribution 0.95 0.95 0.11 asserted 1.00
s2 Notably, our algorithm is the first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a privacy guarantee. result 0.64 0.94 0.60 asserted 1.00
s3 Our method is based on an exponential weights meta-algorithm, and we mitigate the issue of discontinuities in revenue functions via small random perturbations. method 1.00 0.93 0.06 asserted 1.00
s4 As a result of its structural similarity to the exponential mechanism, our method inherently secures differential privacy. method 0.47 0.81 0.08 asserted 1.00
s5 We also extend our algorithm to accommodate scenarios where buyers strategically bid over successive rounds. contribution 0.99 0.93 0.04 asserted 0.99
s6 The inherent differential privacy allows us to adapt our algorithm with minimal modification to ensure a sublinear regret in this setting. contribution 0.46 0.90 0.17 asserted 0.99

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
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

This work addresses the problem of revenue maximization in a repeated, unlimited supply item-pricing auction while preserving buyer privacy. We present a novel algorithm that provides differential privacy with respect to the buyer's input pair: item selection and bid. Notably, our algorithm is the first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a privacy guarantee. Our method is based on an exponential weights meta-algorithm, and we mitigate the issue of discontinuities in revenue functions via small random perturbations. As a result of its structural similarity to the exponential mechanism, our method inherently secures differential privacy. We also extend our algorithm to accommodate scenarios where buyers strategically bid over successive rounds. The inherent differential privacy allows us to adapt our algorithm with minimal modification to ensure a sublinear regret in this setting.

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