STAMP: Selective Task-Aware Mechanism for Text Privacy

• URL: http://arxiv.org/abs/2603.12237v1
• Date: Thu, 12 Mar 2026 17:55:07 GMT
• Title: STAMP: Selective Task-Aware Mechanism for Text Privacy
• Abstract summary: STAMP is a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off.<n>We introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude.<n> Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy-utility trade-offs.
• Score: 5.453215632053115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) each token's importance to the downstream task (as measured via a task- or query-specific representation), and (ii) its privacy sensitivity (e.g., names, dates, identifiers). This token-level partitioning enables fine-grained, group-wise control over the level of noise applied to different parts of the input, balancing privacy protection with task relevance. To privatize individual token embeddings, we introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude. Decoding is performed via cosine nearest-neighbor search, aligning the perturbation geometry with the decoding geometry. Unlike isotropic noise mechanisms, the polar mechanism maintains semantic neighborhoods in the embedding space and better preserves downstream utility. Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy-utility trade-offs across varying per-token privacy budgets.

Related papers

• Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection [14.631956899935721]
  Video anomaly detection (VAD) systems often prioritize accuracy while overlooking privacy concerns, limiting their suitability for real-world deployment.<n>We propose the Orthogonal Projection Layer (OPL), a lightweight module that removes task-irrelevant variations to produce representations focused on anomaly-relevant cues.<n>To address privacy risks in human-centered scenarios, we introduce Guided OPL (G-OPL), which suppresses facial attributes using weak supervision from face-presence signals while preserving non-identifying features such as pose and motion.
  arXiv  Detail & Related papers  (2026-05-09T03:46:39Z)
• CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras [16.147944008359957]
  CityGuard is a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance.<n>A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness.<n>Private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment.
  arXiv  Detail & Related papers  (2026-02-20T08:00:17Z)
• DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization [26.29089564225218]
  We propose a framework incorporating an Adaptive Differentially Private Entity Sanitization algorithm.<n>Our method achieves near-perfect detection accuracy, significantly outperforming non-private baselines.
  arXiv  Detail & Related papers  (2026-01-08T06:33:15Z)
• Breaking the Gaussian Barrier: Residual-PAC Privacy for Automatic Privatization [27.430637970345433]
  We show that the upper bound obtained by PAC Privacy algorithms is tight if and only if the perturbed mechanism output is jointly Gaussian with independent noise.<n>We introduce Residual-PAC (R-PAC) Privacy, an f-divergence-based measure to quantify privacy that remains after adversarial inference.<n>Our approach achieves efficient privacy budget utilization for arbitrary data distributions and naturally composes when multiple mechanisms access the dataset.
  arXiv  Detail & Related papers  (2025-06-06T20:52:47Z)
• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
  Amplification by subsampling is one of the main primitives in machine learning with differential privacy. We propose the first general framework for deriving mechanism-specific guarantees. We analyze how subsampling affects the privacy of groups of multiple users.
  arXiv  Detail & Related papers  (2024-03-07T19:36:05Z)
• Adaptive Privacy Composition for Accuracy-first Mechanisms [55.53725113597539]
  Noise reduction mechanisms produce increasingly accurate answers. Analysts only pay the privacy cost of the least noisy or most accurate answer released. There has yet to be any study on how ex-post private mechanisms compose. We develop privacy filters that allow an analyst to adaptively switch between differentially private and ex-post private mechanisms.
  arXiv  Detail & Related papers  (2023-06-24T00:33:34Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• Graph-Homomorphic Perturbations for Private Decentralized Learning [64.26238893241322]
  Local exchange of estimates allows inference of data based on private data. perturbations chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible.
  arXiv  Detail & Related papers  (2020-10-23T10:35:35Z)
• Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics [2.614355818010333]
  We provide strong privacy loss bounds when an analyst may select pure DP, bounded range (e.g. exponential mechanisms) or concentrated DP mechanisms in any order. We also provide optimal privacy loss bounds that apply when an analyst can select pure DP and bounded range mechanisms in a batch.
  arXiv  Detail & Related papers  (2020-04-15T17:33:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.