Author

Navnit Shukla

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#machine learning Preprint Sep 2026

Privacy-Friendly Cohort Determination: Sealed, CSP-Independent In-Browser ML Inference of Professional Segments for Identity-Less Advertising

SIF (Sealed Inference Frame), which infers coarse professional cohorts on the device and emits only a locally differentially private, taxonomy-coded label into the OpenRTB bid stream, with no cross-site identifier, is presented.

Omsankar Tiwari, Navnit Shukla, Guan-Yu Wang et al. · 0 citations

Jul 2026

TurboVec: A Case Study in Cost-Efficient Private Retrieval for Enterprise RAG via Codebook-Oblivious Quantization

TurboVec, an open-source vector index built on TurboQuant - a codebook-oblivious scalar quantizer requiring no corpus-dependent training is studied, which achieves 11ms median query latency at 100K vectors versus 707ms for warehouse brute-force scan.

Navnit Shukla, Kamal Pandey, Omsankar Tiwari · arXiv.org · 1 citation