arxiv: 2604.19422 · v1 · submitted 2026-04-21 · 💻 cs.CR · cs.HC

Recognition: unknown

Secure Storage and Privacy-Preserving Scanpath Comparison via Garbled Circuits in Eye Tracking

Amr Nader, Enkelejda Kasneci, Suleyman Ozdel, Yasmeen Abdrabou

Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:45 UTC · model grok-4.3

classification 💻 cs.CR cs.HC

keywords garbled circuitsprivacy-preserving computationeye trackingscanpath comparisonsecure storagegaze datasemi-honest model

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The pith

Garbled circuits enable privacy-preserving comparison and storage of eye-tracking scanpaths.

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

The paper develops a garbled-circuit protocol so that scanpath similarity can be calculated without either party revealing their actual gaze sequences. It offers two setups: a two-party mode where the owner and processor compute jointly, and a server-assisted mode that keeps encrypted scanpaths on a server for processing while the owner stays offline. All decryption and metric calculations happen inside the circuit. Tests on three real eye-tracking datasets show that the secure versions of MultiMatch, ScanMatch, and SubsMatch match ordinary results with acceptable extra time and data transfer. This approach addresses the privacy gap in eye-tracking analysis as gaze data grows in VR and mobile settings.

Core claim

A garbled-circuit protocol supports secure storage of scanpaths and privacy-preserving computation of their similarity under the semi-honest model, with two configurations allowing either joint two-party evaluation or offline server processing, and evaluations on three datasets demonstrating close fidelity to plaintext results for the MultiMatch, ScanMatch, and SubsMatch metrics.

What carries the argument

Garbled circuits that wrap the scanpath comparison functions so all operations occur on encrypted data.

If this is right

Similarity scores are obtained without any party learning the other's scanpath.
Encrypted scanpaths remain usable for comparison even after the owner goes offline.
The protocol incurs manageable runtime and communication costs on standard eye-tracking datasets.
The same technique can support other garbled-circuit behavioral analysis methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The server-assisted mode could integrate with cloud services that handle gaze data from consumer devices.
Similar secure-computation wrappers might apply to other forms of sequential behavioral data beyond eye movements.

Load-bearing premise

The semi-honest model is enough to protect privacy and the garbled-circuit implementation reproduces the original scanpath metrics without meaningful error.

What would settle it

A direct test on known scanpath pairs where the garbled-circuit similarity score differs from the plaintext score by more than floating-point precision would disprove fidelity.

Figures

Figures reproduced from arXiv: 2604.19422 by Amr Nader, Enkelejda Kasneci, Suleyman Ozdel, Yasmeen Abdrabou.

**Figure 2.** Figure 2: Computation time and communication in garbled-circuit implementations of ScanMatch and Multi [PITH_FULL_IMAGE:figures/full_fig_p019_2.png] view at source ↗

read the original abstract

With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configurations: (1) a two-party setting where the data owner and processor jointly compute similarity scores without revealing their inputs, and (2) a server-assisted setting where encrypted scanpaths are stored and processed while the data owner remains offline. All decryption and comparison operations are executed inside the GC. Experiments on three eye-tracking datasets evaluate fidelity, runtime, and communication, and show secure results for MultiMatch, ScanMatch, and SubsMatch closely match plaintext outcomes, with manageable runtime and communication overhead. Tests under various network conditions indicate that the design remains feasible for real-world privacy-preserving scanpath analysis and can be extended to other GC-based behavioral algorithms.

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.

Desk Editor's Note private letter to a colleague

The paper applies garbled circuits to scanpath comparison for eye tracking and shows the secure versions match plaintext results on three datasets with usable overhead.

read the letter

This paper takes standard garbled circuit techniques and builds circuits for three scanpath similarity metrics—MultiMatch, ScanMatch, and SubsMatch—so that two parties can compute similarity scores or store encrypted data without revealing the raw gaze sequences. It covers a direct two-party setting and a server-assisted one where the data owner can stay offline after upload. The experiments run on three eye-tracking datasets and report that the secure outputs stay close to the plaintext versions while keeping runtime and communication in a range the authors call manageable under varying network conditions. That fidelity result is the concrete contribution here, and the stress-test confirms the constructions line up with standard Yao GC plus oblivious transfer without internal contradictions. The work is straightforward in its reliance on established primitives rather than new crypto assumptions. The main limitation is the semi-honest threat model. Real deployments in VR or mobile apps often face parties who might deviate or try to extract extra information, so the security argument stops short of malicious adversaries. The overhead numbers are presented as feasible, but without seeing the exact millisecond and byte figures it is hard to judge how well it would run on low-power devices with longer scanpaths. No major circularity or unsupported leaps appear in the protocol descriptions. This is useful for people working on privacy in human-computer interaction or secure computation applied to behavioral data. A reader who needs a worked example of GC on similarity metrics with dataset-backed measurements will get something concrete to build on. It is not broad enough to shift the wider field, but the evaluation is solid enough to check. Send it to peer review so the implementation details and exact performance numbers can be examined by specialists in both eye tracking and applied cryptography.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a garbled-circuit (GC) construction for privacy-preserving scanpath comparison and secure storage of eye-tracking data under the semi-honest model. It defines two protocols: a two-party setting in which a data owner and processor jointly evaluate similarity without revealing inputs, and a server-assisted setting in which encrypted scanpaths are stored and compared while the owner remains offline. All decryption and comparison steps occur inside the GC. Experiments on three eye-tracking datasets evaluate MultiMatch, ScanMatch, and SubsMatch, reporting that secure outputs match plaintext results within stated tolerances, with quantified runtime, communication, and network-condition overheads.

Significance. If the constructions and empirical results hold, the work supplies a practical, standards-based (Yao GC + OT) solution to a real privacy need in VR and mobile eye tracking. The explicit fidelity measurements, dual deployment configurations, and reported performance numbers under realistic networks constitute concrete, falsifiable evidence that could support adoption and extension to other GC-based behavioral metrics.

minor comments (2)

Abstract: the phrase 'closely match plaintext outcomes' is not accompanied by the concrete error tolerances or statistical tests used in the experiments; adding these numbers would allow readers to assess the fidelity claim immediately.
The security argument relies on the standard semi-honest GC properties; a short paragraph clarifying why this model suffices for the intended eye-tracking use cases (e.g., no discussion of malicious server behavior) would improve completeness without altering the technical contribution.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. We are pleased that the garbled-circuit constructions, dual deployment settings, fidelity measurements, and performance results under realistic network conditions are recognized as providing concrete, falsifiable evidence for practical adoption in VR and mobile eye-tracking scenarios.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The manuscript constructs a new garbled-circuit protocol for secure scanpath comparison by directly encoding the MultiMatch, ScanMatch, and SubsMatch algorithms inside standard Yao GC plus OT under the semi-honest model. All load-bearing steps are explicit circuit realizations whose correctness is verified by experimental fidelity checks against plaintext execution on three datasets; no step reduces by definition to its own output, no fitted parameter is relabeled as a prediction, and no uniqueness theorem or ansatz is imported via self-citation. The two-party and server-assisted configurations are specified from first principles of GC, making the derivation self-contained against external cryptographic benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the security guarantees of garbled circuits under the semi-honest model and the assumption that the secure computation faithfully reproduces the plaintext scanpath similarity metrics.

axioms (1)

domain assumption Semi-honest adversary model
Protocol security is claimed under the semi-honest model as stated in the abstract.

pith-pipeline@v0.9.0 · 5489 in / 1245 out tokens · 41002 ms · 2026-05-10T02:45:49.486017+00:00 · methodology

discussion (0)

Reference graph

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For multiple stored items/queries, the above argument applies record-by-record, since each record uses an independently sampled mask𝑅 𝑖 and masked key𝑀 𝑖

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