Demystifying the Privacy-Utility Trade-off in LLM Interactions

URL SCAN: Demystifying the Privacy-Utility Trade-off in LLM Interactions
FIRST LINE: # Computer Science > Artificial Intelligence

The Dissection

This paper treats privacy as a context-engineering problem. It decomposes sanitization into three mechanisms—intent-dependent value, removal versus replacement, and attribute interaction—then packages them into an extraction-sanitization-restoration pipeline driven by Veilmind-4B. The reported contribution is a better operating point on a measured privacy-utility curve, not the elimination of exposure.

The deeper move is normalization: sensitive context is assumed to flow into LLM systems, and the research question becomes how efficiently to disguise it.

The Core Fallacy

The paper optimizes the boundary instead of controlling the sovereign.

A sanitizer must inspect raw context to infer intent. That makes the sanitizer a privileged observer and a new attack surface. Removing an explicit identifier does not prevent inference from correlated attributes; replacing information can preserve enough structure for reconstruction; restoration can reintroduce sensitive material later in the pipeline. “Low leakage” is not privacy, and a Pareto improvement on selected tasks is not a durable defense against adversarial inference, distribution shift, logging, retention, model training, or cross-session linkage.

Under the Discontinuity Thesis, the larger error is treating better privacy middleware as if it preserved human control over AI. It does not. It makes centralized cognitive automation easier to use and easier to deploy.

Hidden Assumptions

• User intent can be inferred correctly before sensitive data is exposed.
• Sensitive attributes can be separated from the utility-bearing structure that enables re-identification.
• Replacement preserves task value without preserving inferential value.
• Leakage can be measured adequately with the chosen evaluations.
• The local model, restoration stage, provider, logs, plugins, and downstream outputs are all within the trusted boundary.
• Users will accept the computational cost and complexity of dynamic protection.
• A technical reduction in disclosure translates into durable privacy under changing attacks.
• Preserving interaction utility preserves meaningful economic agency, despite the ownership of the underlying AI remaining elsewhere.

Social Function

Partial truth functioning as transition management and ideological anesthetic.

The engineering problem is real. Intent-aware sanitization can reduce unnecessary disclosure. But the framing converts structural extraction into a solvable interface defect: install a smarter veil and continue feeding the machine. It makes dependence on LLM infrastructure appear governable while leaving ownership, telemetry, compute concentration, and control untouched. The paper is respectable privacy engineering wrapped around a system that remains fundamentally extractive.

The Verdict

Useful as a local privacy component. Terminally insufficient as a systemic answer.

This work may reduce leakage in bounded interactions, but it does not challenge P1, P2, or P3. It cannot prevent cognitive automation from severing the mass employment-wage-consumption circuit, nor can it create a stable human-controlled economic domain at scale. It is a better air filter fitted to the exhaust pipe of the AI economy: valuable for exposure reduction, irrelevant to who owns the engine.