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Showing 1 to 4 of 4 for “"privacy focused"”.

  1. Privacy-Focused LLM for local data processing: Implementing OLLAMA and RAG to securely query personal files in closed environments

    … project addresses the growing concerns of data privacy and security associated with cloud-based AI systems by developing a locally hosted, privacy-preserving AI framework. The solution is designed to provide advanced AI functionalities, ensuring organizations retain full control over their …

    catalunya Repository record for Privacy-Focused LLM for local data processing: Implementing OLLAMA and RAG to securely query personal files in closed environments (opens in a new tab)

  2. Data Centric Defenses for Privacy Attacks

    … used in model training. These attacks called privacy attacks, exploit the model training process. Contemporary defense techniques make alterations to the training algorithm. Such defenses are computationally expensive, cause a noticeable privacy-utility tradeoff, and require control over the …

    vt Repository record for Data Centric Defenses for Privacy Attacks (opens in a new tab)

  3. Privacy-Preserving Application of Blockchain Technologies

    … questions and concerns regarding user and data privacy; An important topic that is typically overlooked due to the lack of proper standards and procedures in an under-regulated technology. Identity theft and data breaches are already impacting users on the internet since enterprises are …

    houston Repository record for Privacy-Preserving Application of Blockchain Technologies (opens in a new tab)

  4. Generating Trustworthy Synthetic Data

    … Real data can be severely limiting: it may be privacy-sensitive, unfair, unbalanced, unrepresentative, or it may simply not exist for the setting of interest. These limitations continuously constrain how the machine learning (ML) community operates---from training to testing, from model …

    cambridge Repository record for Generating Trustworthy Synthetic Data (opens in a new tab)