Privacy-preserving linear algebra techniques refer to methods that enable the computation and analysis of data while ensuring the privacy and confidentiality of sensitive information. These techniques are increasingly important as data-driven approaches become more common in various fields, requiring robust solutions that can protect individual privacy while still allowing for meaningful data insights.
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