Multi-granularity refers to the capability of a system to represent or process data at different levels of detail or resolution. In computing and data management, this allows for flexible analysis and presentation, from broad overviews to fine-grained specifics. It enables users to select the appropriate level of information for their needs. This approach supports diverse analytical requirements.
Context
Multi-granularity can be relevant in blockchain analytics and data indexing, where information might be viewed at a transaction level, block level, or aggregated across entire networks. News reports might discuss tools or protocols that offer multi-granularity for tracking digital asset flows or network activity. The benefit lies in providing adaptable data insights for various users, from forensic analysts to market researchers. Developments in this area enhance transparency and analytical depth.
zkUnlearner introduces a bit-masking technique for zero-knowledge proofs, enabling verifiable, multi-granular data unlearning in AI models and resisting forging attacks.
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