Poster (Scientific congresses, symposiums and conference proceedings)
Unlinkable Updatable Hiding Databases and Privacy Preserving Loyalty Programs
DAMODARAN, Aditya Shyam Shankar; RIAL, Alfredo
2021Privacy Enhancing Technologies Symposium
 

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Keywords :
UC Framework; Vector Commitments
Abstract :
[en] Loyalty programs allow vendors to profile buyers based on their purchase histories, which can reveal privacy sensitive information. Existing privacy friendly loyalty programs force buyers to choose whether their purchases are linkable. Moreover, vendors receive more purchase data than required for the sake of profiling. We propose a privacy-preserving loyalty program where purchases are always unlinkable, yet a vendor can profile a buyer based on her purchase history, which remains hidden from the vendor. Our protocol is based on a new building block, an unlinkable updatable hiding database (HD), which we define and construct. HD allows the vendor to initialize and update databases stored by buyers that contain their purchase histories and their accumulated loyalty points. Updates are unlinkable and, at each update, the database is hidden from the vendor. Buyers can neither modify the database nor use old versions of it. Our construction for HD is practical for large databases.
Research center :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > APSIA - Applied Security and Information Assurance
Disciplines :
Computer science
Author, co-author :
DAMODARAN, Aditya Shyam Shankar ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > APSIA
RIAL, Alfredo ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > APSIA
External co-authors :
no
Language :
English
Title :
Unlinkable Updatable Hiding Databases and Privacy Preserving Loyalty Programs
Publication date :
July 2021
Event name :
Privacy Enhancing Technologies Symposium
Event place :
Rochester, United States - New York
Event date :
from 12-07-2021 to 16-07-2021
Focus Area :
Computational Sciences
FnR Project :
FNR11650748 - Stateful Zero-knowledge, 2017 (01/03/2018-28/02/2021) - Alfredo Rial
Name of the research project :
Stateful Zero Knowledge
Funders :
FNR - Fonds National de la Recherche
Available on ORBilu :
since 01 July 2023

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