Membership Inference Attacks against Language Models via Neighbourhood Comparison

Justus, Mattern,; Mireshghallah, Fatemehsadat; Jin, Zhijing; Schoelkopf, Bernhard; Sachan, Mrinmaya; Berg-Kirkpatrick, Taylor

doi:10.18653/v1/2023.findings-acl.719

Cited by 11 publications

(1 citation statement)

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“…This includes the potential for models to memorise and replicate exact snippets of sensitive training data [60]. Furthermore, inference attacks, such as model inversion or membership inference attacks, can be used to extract detailed information about the training data or about individuals' data used in the training set, leading to privacy violations [61].…”

Section: Limitationsmentioning

confidence: 99%

Leveraging Large Language Models for Sensor Data Retrieval

Berenguer,

Morejón,

Tomás

et al. 2024

Applied Sciences

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The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it into a reusable structured format, while the second component aims to identify the most relevant sensor data to augment a given user-provided dataset. The evaluation of the proposed approach involved comparing the performance of various large language models in generating representative word embeddings for each table to retrieve related sensor data. The results show promising performance in terms of precision and MRR (0.90 and 0.94 for the best-performing model, respectively), indicating the system’s ability to retrieve pertinent sensor data that fulfil user requirements.

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Section: Limitationsmentioning

confidence: 99%