rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm

• URL: http://arxiv.org/abs/2105.08842v1
• Date: Tue, 18 May 2021 21:50:12 GMT
• Title: rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm
• Abstract summary: rx-anon is an anonymization approach for heterogeneous semi-structured documents composed of relational and textual attributes. We map sensitive terms extracted from the text to the structured data. We introduce the concept of redundant sensitive information to consistently anonymize the data.
• Score: 3.077802365568915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional approaches for data anonymization consider relational data and textual data independently. We propose rx-anon, an anonymization approach for heterogeneous semi-structured documents composed of relational and textual attributes. We map sensitive terms extracted from the text to the structured data. This allows us to use concepts like k-anonymity to generate a joined, privacy-preserved version of the heterogeneous data input. We introduce the concept of redundant sensitive information to consistently anonymize the heterogeneous data. To control the influence of anonymization over unstructured textual data versus structured data attributes, we introduce a modified, parameterized Mondrian algorithm. The parameter $\lambda$ allows to give different weight on the relational and textual attributes during the anonymization process. We evaluate our approach with two real-world datasets using a Normalized Certainty Penalty score, adapted to the problem of jointly anonymizing relational and textual data. The results show that our approach is capable of reducing information loss by using the tuning parameter to control the Mondrian partitioning while guaranteeing k-anonymity for relational attributes as well as for sensitive terms. As rx-anon is a framework approach, it can be reused and extended by other anonymization algorithms, privacy models, and textual similarity metrics.

Related papers

• Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data [56.86147283213652]
  We present a customizable empirical auditing framework designed to detect and explain data disclosures.<n>Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures"-where the system incidentally generates a user's data.
  arXiv  Detail & Related papers  (2026-06-15T16:54:02Z)
• Statistical Embeddings for Similarity, Retrieval, and Interpretable Alignment of Numeric Tabular Datasets [0.0]
  The proposed methodology characterizes numeric datasets through structured exploratory data analysis descriptors.<n>A penalized formulation of Canonical Correlation Analysis (CCA) is applied to recover sparse, interpretable variable-level correspondences.<n>The methodology is evaluated across 15 datasets spanning general-purpose benchmarks, materials informatics, and nuclear-grade graphite characterization.
  arXiv  Detail & Related papers  (2026-05-28T17:40:42Z)
• When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR [17.93682540686544]
  SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice.<n>A model adapted to recognise domain-specific terminology can be nudged into transcribing a phonetically similar word from its context.<n>We evaluate a prompt-level mitigation strategy and analyse the accuracy-leakage trade-off across customisation approaches.
  arXiv  Detail & Related papers  (2026-05-27T09:30:36Z)
• Generative Data Transformation: From Mixed to Unified Data [57.84692191369066]
  textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
  arXiv  Detail & Related papers  (2026-02-26T08:30:09Z)
• Learning from Anonymized and Incomplete Tabular Data [2.8026354138147513]
  User-driven privacy allows individuals to control whether and at what granularity their data is shared.<n> datasets that mix original, generalized, and missing values pose challenges for machine learning.<n>We propose novel data transformation strategies that account for heterogeneous anonymization.
  arXiv  Detail & Related papers  (2026-02-01T13:18:14Z)
• Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization [55.29071072675132]
  Face anonymization aims to conceal identity information while preserving non-identity attributes.<n>We propose textbfIDsuperscript2Face, a training-centric anonymization framework.<n>We show that IDtextsuperscript2Face outperforms existing methods in visual quality, identity suppression, and utility preservation.
  arXiv  Detail & Related papers  (2025-10-28T09:28:12Z)
• Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search [60.197239728279534]
  Large language models (LLMs) in cloud-based services have raised significant privacy concerns.<n>Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility.<n>We propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness.
  arXiv  Detail & Related papers  (2025-09-25T07:23:52Z)
• Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization [5.239989658197324]
  We present Tau-Eval, an open-source framework for benchmarking text anonymization methods.<n>A Python library, code, documentation and tutorials are publicly available.
  arXiv  Detail & Related papers  (2025-06-06T10:59:35Z)
• Self-Refining Language Model Anonymizers via Adversarial Distillation [49.17383264812234]
  Large language models (LLMs) are increasingly used in sensitive domains, where their ability to infer personal data poses emerging privacy risks.<n>We introduce SElf-refining Anonymization with Language model (SEAL), a novel distillation framework for training small language models (SLMs) to perform effective anonymization.
  arXiv  Detail & Related papers  (2025-06-02T08:21:27Z)
• Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity [40.64395367773766]
  We propose a distributed strong lottery ticket hypothesis (TH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the Hadamard product of global real-valued parameters and a personalized binary mask for pruning. We provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method.
  arXiv  Detail & Related papers  (2025-04-24T13:02:54Z)
• Prompt-Driven and Training-Free Forgetting Approach and Dataset for Large Language Models [4.824120664293887]
  We propose an Automatic dataset Creation Framework based on prompt-based layered editing and training-free local feature removal. The ForgetMe dataset encompasses a diverse set of real and synthetic scenarios, including CUB-200-2011 (Birds), Stanford-Dogs, ImageNet, and a synthetic cat dataset. We apply LoRA fine-tuning on Stable Diffusion to achieve selective unlearning on this dataset and validate the effectiveness of both the ForgetMe dataset and the Entangled metric.
  arXiv  Detail & Related papers  (2025-04-17T01:44:57Z)
• DUEF-GA: Data Utility and Privacy Evaluation Framework for Graph Anonymization [0.0]
  We propose a framework to evaluate and compare anonymous datasets in a common way. Our framework includes metrics based on generic information loss measures, such as average distance or betweenness centrality. We demonstrate that our framework could help researchers and practitioners to select the best parametrization and/or algorithm.
  arXiv  Detail & Related papers  (2025-01-27T12:22:40Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Text Attribute Control via Closed-Loop Disentanglement [72.2786244367634]
  We propose a novel approach to achieve a robust control of attributes while enhancing content preservation. In this paper, we use a semi-supervised contrastive learning method to encourage the disentanglement of attributes in latent spaces. We conducted experiments on three text datasets, including the Yelp Service review dataset, the Amazon Product review dataset, and the GoEmotions dataset.
  arXiv  Detail & Related papers  (2023-12-01T01:26:38Z)
• Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution [1.6385815610837167]
  We propose a novel blind annotation protocol based on homomorphic encryption.<n>We show that our protocol achieves more than 90% compared with the real ground truths.
  arXiv  Detail & Related papers  (2023-08-07T17:32:33Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)
• Fair mapping [0.0]
  We propose a novel pre-processing method based on the transformation of the distribution of protected groups onto a chosen target one. We leverage on the recent works of the Wasserstein GAN and AttGAN frameworks to achieve the optimal transport of data points. Our proposed approach, preserves the interpretability of data and can be used without defining exactly the sensitive groups.
  arXiv  Detail & Related papers  (2022-09-01T17:31:27Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation [6.61544170496402]
  Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain. We propose a novel unsupervised DA method, namely Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation.
  arXiv  Detail & Related papers  (2021-01-09T07:10:13Z)
• Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach [4.920145245773581]
  In this paper, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data. We show that it can anonymize data in real time on resource-constrained edge devices.
  arXiv  Detail & Related papers  (2020-11-16T22:32:30Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Learning Flat Latent Manifolds with VAEs [16.725880610265378]
  We propose an extension to the framework of variational auto-encoders, where the Euclidean metric is a proxy for the similarity between data points. We replace the compact prior typically used in variational auto-encoders with a recently presented, more expressive hierarchical one. We evaluate our method on a range of data-sets, including a video-tracking benchmark.
  arXiv  Detail & Related papers  (2020-02-12T09:54:52Z)

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.