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arxiv: 2506.00282 · v1 · submitted 2025-05-30 · 💻 cs.GT · cs.CR· cs.SE

Shill Bidding Prevention in Decentralized Auctions Using Smart Contracts

M.A. Bouaicha , G. Destefanis , T. Montanaro , N. Lasla , L. Patrono This is my paper

Pith reviewed 2026-05-19 12:22 UTC · model grok-4.3

classification 💻 cs.GT cs.CRcs.SE
keywords shill biddingsmart contractsdecentralized auctionsbid shill scoreblockchainfraud preventionethereumenglish auction
0
0 comments X

The pith

A smart contract system uses a Bid Shill Score to apply dynamic penalties that deter shill bidding in decentralized auctions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a framework that prevents shill bidding in online auctions by applying penalties in real time rather than detecting fraud after the sale ends. Smart contracts on the Ethereum blockchain monitor bids and adjust penalty fees according to how suspicious each bidder's pattern appears. The system scores nine specific bidding behaviors through a metric called the Bid Shill Score to decide the size of each penalty. This design aims to make fraudulent activity too costly for cheaters while leaving honest participants largely unaffected. Simulations indicate the approach lowers the gains from shill bidding and adds only moderate transaction costs and delays.

Core claim

The authors propose a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviors, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, and simulations show

What carries the argument

The Bid Shill Score (BSS), which evaluates nine bidding behaviors to dynamically scale penalty fees inside the smart contract.

Load-bearing premise

The nine bidding behaviors can be reliably detected and scored in real time by the smart contract without producing high false-positive rates that would unfairly penalize legitimate bidders.

What would settle it

A simulation or deployment in which shill bidders still achieve positive net profit or honest bidders receive frequent penalties would show the mechanism fails to work as intended.

Figures

Figures reproduced from arXiv: 2506.00282 by G. Destefanis, L. Patrono, M.A. Bouaicha, N. Lasla, T. Montanaro.

Figure 1
Figure 1. Figure 1: Summary of auction types 3. First-Price Sealed-Bid Auction (FPSB): In an FPSB, all bidders place their bids at the same time and in private, and each is allowed only one bid [14]. The auction ends with the determination of the highest bidder as the winner. This type requires very strategic planning because the bidders have to get the best out of both submitting a competitive bid and not overspending [16]. … view at source ↗
Figure 2
Figure 2. Figure 2: English auction process Each auction type has advantages and limitations, making it important to select the format that best suits the needs of buyers and sellers. However, all auctions are vulnerable to fraudulent practices that damages fairness and efficiency. SB is particularly common in English auctions, where open bidding makes it difficult to distinguish between fraudulent and legitimate participants… view at source ↗
Figure 3
Figure 3. Figure 3: Example of shill bidding scenario [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The proposed auction system architecture [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Class diagram of the blockchain-based auction system [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sequence diagram for interactions in the auction process [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Create and activate auction output details [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: placeBid function output details 3. Auction Termination The endAuction function ensures that the auction closes once the predefined duration has elapsed, preventing further bids. As shown in [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: endAuction function output details 6. Discussion The decision to implement SB prevention through smart contracts ensures a transparent, decentralized, and tamper-proof mechanism. Smart contracts serve as a consensus layer between bidders and sellers, enforcing predefined rules without requiring a trusted third party. This makes fraudulent auction manipulation unprofitable for dishonest sellers while discou… view at source ↗
Figure 10
Figure 10. Figure 10: BSS results across 50 auction simulations for Single-Account, Multi-Account, and Time-Collusion [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Evaluation of gas usage, latency, and scalability of system functions [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
read the original abstract

In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviors, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a conceptual framework for real-time shill bidding prevention in decentralized English auctions on Ethereum using smart contracts. It introduces a Bid Shill Score (BSS) computed from nine bidding behaviors (e.g., timing, increments, patterns) to dynamically scale penalty fees, making fraudulent activity unprofitable while keeping penalties low for honest bidders. The system is implemented via smart contracts enforcing transparent rules without intermediaries, with simulations claimed to confirm reduced shill profitability, low honest penalties, and only moderate gas/latency overhead.

Significance. If the simulations and on-chain detection assumptions hold under realistic conditions, the work offers a practical economic disincentive mechanism for fraud prevention in trustless blockchain auctions. This contributes to mechanism design and smart-contract security by shifting from post-auction detection to real-time behavioral penalties, potentially improving fairness in decentralized marketplaces. The emphasis on Ethereum implementation and performance metrics provides a concrete starting point for applied research at the intersection of game theory and blockchain.

major comments (3)
  1. [Simulations / Performance Evaluation] Simulations section: The central claim that the dynamic penalty mechanism reduces shill bidding profitability while keeping penalties low for honest bidders rests on simulations, yet no details are provided on simulation parameters, baseline comparisons (e.g., static penalty or no-penalty auctions), or the formal definitions and detection rules for the nine bidding behaviors. This absence makes the quantitative results difficult to evaluate or reproduce independently.
  2. [Smart Contract Implementation / BSS Evaluation] BSS evaluation and smart contract implementation: The framework assumes the smart contract can reliably compute the Bid Shill Score from on-chain bid history in real time without high false-positive rates that would unfairly penalize legitimate bidders. No analysis of gas costs for persistent pattern tracking, storage requirements, or sensitivity to detection errors is given, which directly bears on whether honest penalties remain low in a practical Ethereum deployment.
  3. [Proposed Framework / BSS Definition] Framework definition: The Bid Shill Score (BSS) and penalty scaling factor are introduced as new constructs rather than derived from prior auction-theoretic quantities or fitted data; the scaling factor is listed as a free parameter. This raises questions about whether the reported balance between deterrence and fairness is robust or sensitive to parameter choice.
minor comments (2)
  1. [Abstract / Introduction] The abstract states that nine behaviors are evaluated but does not list or categorize them; adding a brief table or enumeration in the main text would improve clarity for readers.
  2. [Proposed Framework] Notation for BSS and penalty functions should be defined with explicit equations early in the framework section to avoid ambiguity when discussing dynamic adjustment.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We will revise the manuscript to address the concerns about simulation reproducibility, smart contract practicality, and framework parameter robustness. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [Simulations / Performance Evaluation] Simulations section: The central claim that the dynamic penalty mechanism reduces shill bidding profitability while keeping penalties low for honest bidders rests on simulations, yet no details are provided on simulation parameters, baseline comparisons (e.g., static penalty or no-penalty auctions), or the formal definitions and detection rules for the nine bidding behaviors. This absence makes the quantitative results difficult to evaluate or reproduce independently.

    Authors: We agree that greater detail is required for independent evaluation and reproducibility. In the revised version we will expand the Simulations section to specify all simulation parameters (including bidder population sizes, auction durations, bid value and timing distributions, and modeled shill strategies), to present explicit baseline comparisons against both no-penalty and static-penalty auctions, and to supply formal definitions together with the exact detection thresholds and rules for each of the nine bidding behaviors that contribute to the BSS. These additions will make the quantitative claims directly verifiable. revision: yes

  2. Referee: [Smart Contract Implementation / BSS Evaluation] BSS evaluation and smart contract implementation: The framework assumes the smart contract can reliably compute the Bid Shill Score from on-chain bid history in real time without high false-positive rates that would unfairly penalize legitimate bidders. No analysis of gas costs for persistent pattern tracking, storage requirements, or sensitivity to detection errors is given, which directly bears on whether honest penalties remain low in a practical Ethereum deployment.

    Authors: We acknowledge the importance of deployment metrics. The revised manuscript will include a dedicated implementation analysis subsection that reports estimated gas costs for maintaining persistent bid-history state and for on-chain BSS computation, quantifies storage overhead on Ethereum, and presents a sensitivity study of detection-error rates (false positives) and their effect on honest-bidder penalties. These results, obtained from our existing simulation framework extended with cost models, will substantiate that penalties for legitimate bidders remain low under realistic assumptions. revision: yes

  3. Referee: [Proposed Framework / BSS Definition] Framework definition: The Bid Shill Score (BSS) and penalty scaling factor are introduced as new constructs rather than derived from prior auction-theoretic quantities or fitted data; the scaling factor is listed as a free parameter. This raises questions about whether the reported balance between deterrence and fairness is robust or sensitive to parameter choice.

    Authors: The BSS is deliberately formulated as a novel, behavior-driven heuristic suited to real-time smart-contract enforcement; it synthesizes documented shill-bidding patterns rather than being derived from classical auction-theoretic primitives. The scaling factor is intentionally left as a tunable design parameter to accommodate different market contexts. To address robustness concerns, the revision will add a parameter-sensitivity study that varies the scaling factor over a practical range and reports the resulting changes in shill-bidder profitability and honest-bidder penalties, thereby demonstrating that the desired balance is maintained for reasonable parameter selections. revision: yes

Circularity Check

0 steps flagged

No circularity: new constructs and simulation results are independent of inputs

full rationale

The paper introduces the Bid Shill Score (BSS) as a novel construct that scores nine bidding behaviors and uses it to scale dynamic penalties in a smart-contract English auction. Simulations then demonstrate that these penalties reduce shill profitability while remaining low for honest bidders. No derivation chain, equation, or prediction is shown to reduce to a fitted parameter, self-citation, or input by construction; the BSS and penalty rules are defined outright rather than derived from prior results. The central effectiveness claim therefore rests on external simulation outcomes rather than tautological re-labeling of its own definitions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim depends on the definition of nine bidding behaviors and the assumption that rational economic agents will avoid shill bidding once penalties scale with the score; no independent empirical validation of these behaviors is supplied.

free parameters (1)
  • penalty scaling factor
    Controls how quickly the fee grows with the Bid Shill Score; value chosen to balance deterrence against honest-bidder impact.
axioms (1)
  • domain assumption Bidders respond rationally to increasing financial penalties by reducing fraudulent activity.
    The deterrence effect rests on this standard economic rationality premise invoked when claiming reduced profitability.
invented entities (1)
  • Bid Shill Score (BSS) no independent evidence
    purpose: Quantifies suspicious bidding patterns across nine behaviors to trigger penalties.
    New metric introduced by the paper with no external validation or prior literature reference supplied in the abstract.

pith-pipeline@v0.9.0 · 5733 in / 1398 out tokens · 47000 ms · 2026-05-19T12:22:54.244982+00:00 · methodology

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Reference graph

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