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Privacy-Preserving Data Fusion

Longxiu Tian et al.

Marketing Science2026https://doi.org/10.1287/mksc.2023.0068article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

Abstract

This paper proposes a generative methodology enabling companies to combine data sets with strong privacy guarantees and near-optimal performance in customer analytics.

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https://doi.org/https://doi.org/10.1287/mksc.2023.0068

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@article{longxiu2026,
  title        = {{Privacy-Preserving Data Fusion}},
  author       = {Longxiu Tian et al.},
  journal      = {Marketing Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/mksc.2023.0068},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.