Privacy-Preserving Data Fusion

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

Authors

Dana Turjeman (ORCID: https://orcid.org/0000-0003-1445-2983)
Longxiu Tian (ORCID: https://orcid.org/0000-0001-6257-7583)

Institutions

University of North Carolina at Chapel Hill (US)
Reichman University (IL)
University of Virginia (US)

Publication Details

Journal: Marketing Science
Published: 2026-04-01
DOI: https://doi.org/10.1287/mksc.2023.0068
Primary Topic: Privacy-Preserving Technologies in Data
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Privacy-Preserving Data Fusion

Dana Turjeman, Longxiu Tian

Apr 1, 2026

Marketing Science

Privacy-Preserving Technologies in Data

article

Privacy-Preserving Data Fusion

Dana Turjeman, Longxiu Tian

April 1, 2026article en

Abstract

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

Marketing Science

University of North Carolina at Chapel Hill (US), Reichman University (IL), University of Virginia (US)

Openalex Percentile: Top 25%

Privacy-Preserving Technologies in Data

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Privacy-Preserving Data Fusion — Dana Turjeman, Longxiu Tian · Marketing Science (2026) | TGRS Research Map | TGRS