The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization

Pilán, Ildikó; Lison, Pierre; Øvrelid, Lilja; Papadopoulou, Anthi; Sánchez, David; Batet, Montserrat

doi:10.1162/coli_a_00458

Cited by 18 publications

(7 citation statements)

References 69 publications

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“…In O'Shaughnessy and Lin's study [3], they propose research on privacy protection practice for data mining with multiple data sources, putting forward a framework for reconstructing data and putting it to use in data clustering projects. Leveraging the IoT, information collection can be conducted at any time, from anywhere, using various devices, and through any transmission channel, which shows the necessity of privacy-preservation [6].…”

Section: Applications Of Privacy-preserving Multilingual Comparable C...mentioning

confidence: 99%

“…Table 2 shows the existing research on applications of multilingual comparable corpora in the IoT, including the privacy-preserving aspects in the above review. Paper Summary [3,6,22] Privacy protection practice for data mining and construction corpus for multiple data clustering; multilingual comparable corpus used in language teaching; evaluation of privacy-oriented corpus by use of text anonymization. [4,8,30,36] Privacy protection for medical data, industry data and railway data, less coverage of multilingual comparable data in IoT.…”

Section: Applications Of Privacy-preserving Multilingual Comparable C...mentioning

confidence: 99%

“…Given that the vector space embedding [47] matrix of the uniform language description of the text is Q (obtained by training, so that CW k , UCW m and TCW l , can be converted into a set of embedding semantic vectors CE k , UCE m and TCE l by Equations ( 4)- (6), respectively,…”

Section: Privacy-preserving Text Embedding Under the Unified Languagementioning

confidence: 99%

“…Corpus, a set of wellsampled and processed electronic texts, is the basic resource for studies of linguistics and computational, especially language engineering for applications and devices in the IoT [5]. With the ever-widening coverage of IoT-related devices, the volume of data continues to increase, while the danger to privacy security increases simultaneously [6]. Thus, while the range of data information sources has become more extensive and precise, the involved language communities have become more complex and are faced with the potential risk of privacy protection.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

Weng,

Dong,

Chaomurilige

2024

Mathematics

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With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT.

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Section: Applications Of Privacy-preserving Multilingual Comparable C...mentioning

confidence: 99%

Section: Applications Of Privacy-preserving Multilingual Comparable C...mentioning

confidence: 99%

Section: Privacy-preserving Text Embedding Under the Unified Languagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

Weng,

Dong,

Chaomurilige

2024

Mathematics

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“…In real-world scenarios, obtaining certain data types such as documents containing personal information (e.g., identification cards, medical receipts, and prescription receipts), voice phishing-related audio data for detecting phishing attempts, and documents with substantial defense information can be challenging [17,18,19]. Consequently, researchers frequently rely on synthetic data to generate these datasets.…”

Section: Usermentioning

confidence: 99%

Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction

Koo,

Park,

Lee

et al. 2023

IEEE Access

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In a data-centric AI paradigm, the model performance is enhanced without altering the model architecture, as evidenced by real-world and benchmark dataset demonstrations. With the advancements of large language models (LLM), it has become increasingly feasible to generate high-quality synthetic data, while considering the need to construct fully synthetic datasets for real-world data containing numerous personal information. However, in-depth validation of the solely synthetic data setting has yet to be conducted, despite the increased possibility of models trained exclusively on fully synthetic data emerging in the future. Therefore, we examined the question, "Do data quality control techniques (known to positively impact data-centric AI) consistently aid models trained exclusively on synthetic datasets?". To explore this query, we performed detailed analyses using synthetic datasets generated for speech recognition postprocessing using the BackTranScription (BTS) approach. Our study primarily addressed the potential adverse effects of data quality control measures (e.g., noise injection and balanced data) and training strategies in the context of synthetic-only experiments. As a result of the experiment, we observed the negative effect that the data-centric methodology drops by a maximum of 44.03 points in the fully synthetic data setting.

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Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions

Huang,

Ge,

Xiang

et al. 2024

Int J Network Mgmt

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Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration.

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The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization

Cited by 18 publications

References 69 publications

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction

Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions

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