Privacy Preservation in Gen AI Applications

• URL: http://arxiv.org/abs/2504.09095v1
• Date: Sat, 12 Apr 2025 06:19:37 GMT
• Title: Privacy Preservation in Gen AI Applications
• Authors: Swetha S, Ram Sundhar K Shaju, Rakshana M, Ganesh R, Balavedhaa S, Thiruvaazhi U,
• Abstract summary: Large Language Models (LLMs) may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions.<n>Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information.<n>This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference.<n>It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability of machines to comprehend and produce language that is similar to that of humans has revolutionized sectors like customer service, healthcare, and finance thanks to the quick advances in Natural Language Processing (NLP), which are fueled by Generative Artificial Intelligence (AI) and Large Language Models (LLMs). However, because LLMs trained on large datasets may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions, these capabilities also raise serious privacy concerns. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information, which raises serious concerns about the privacy and security of AI-driven data. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. A privacy-preserving Generative AI application that is resistant to these assaults is then developed. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs. In order to determine how well cloud platforms like Microsoft Azure, Google Cloud, and AWS provide privacy tools for protecting AI applications, the study also examines these technologies. In the end, this study offers a fundamental privacy paradigm for generative AI systems, focusing on data security and moral AI implementation, and opening the door to a more secure and conscientious use of these tools.

Related papers

• AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents [75.85554113398626]
  We develop a benchmark called AgentDAM to evaluate how well existing and future AI agents can limit processing of potentially private information.<n>Our benchmark simulates realistic web interaction scenarios and is adaptable to all existing web navigation agents.
  arXiv  Detail & Related papers  (2025-03-12T19:30:31Z)
• Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions [12.451936012379319]
  Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains.<n>Their reliance on massive internet-sourced datasets for training brings notable privacy issues.<n>Certain application-specific scenarios may require fine-tuning these models on private data.
  arXiv  Detail & Related papers  (2024-08-10T05:41:19Z)
• Unlearning Targeted Information via Single Layer Unlearning Gradient [15.374381635334897]
  Unauthorized privacy-related computation is a significant concern for society. The EU's General Protection Regulation includes a "right to be forgotten" We propose Single Layer Unlearning Gradient (SLUG) to unlearn targeted information by updating targeted layers of a model.
  arXiv  Detail & Related papers  (2024-07-16T15:52:36Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Privacy Risks in Reinforcement Learning for Household Robots [42.675213619562975]
  Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information.<n>This paper proposes an attack on the training process of the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervisory signals.
  arXiv  Detail & Related papers  (2023-06-15T16:53:26Z)
• A Survey on Differential Privacy with Machine Learning and Future Outlook [0.0]
  differential privacy is used to protect machine learning models from any attacks and vulnerabilities. This survey paper presents different differentially private machine learning algorithms categorized into two main categories.
  arXiv  Detail & Related papers  (2022-11-19T14:20:53Z)
• A Survey of Machine Unlearning [56.017968863854186]
  Recent regulations now require that, on request, private information about a user must be removed from computer systems. ML models often remember' the old data. Recent works on machine unlearning have not been able to completely solve the problem.
  arXiv  Detail & Related papers  (2022-09-06T08:51:53Z)
• More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence [62.3133247463974]
  We show that differential privacy can do more than just privacy preservation in AI. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.
  arXiv  Detail & Related papers  (2020-08-05T03:07:36Z)

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.