User Customizable and Robust Geo-Indistinguishability for Location Privacy

Primal Pappachan, Chenxi Qiu, Anna Squicciarini, Vishnu Sharma Hunsur Manjunath

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Geo-Indistinguishability (Geo-Ind), based on Differential Privacy, is a popular privacy notion of privacy used for protecting individual's location data. Existing approaches, to generate a Geo-Ind satisfying obfuscation function, rely on a server, as this generation is computationally expensive. As a result, these obfuscation functions are not modifiable by users and any customization will lead to weakening of the Geo-Ind privacy guarantees i.e., violation of constraints in the function. A non-customizable obfuscation function can map an individual to an undesirable location, leading to poor quality of service. We present a framework called CORGI, i.e., CustOmizable Robust Geo-Indistinguishability, which allows users to customize an obfuscation function and ensure it is robust i.e., after user customization only minimal number of Geo-Ind constraints are violated. The experimental results on a real-world dataset demonstrate the effectiveness of CORGI in generating obfuscation functions that are more robust against customization by users, e.g., removing 14.28% of locations from the range of the obfuscation function leads to 18.58% and 3.07% Geo-Indistinguishability constraint violations, when the obfuscation function is generated by prior approaches and CORGI respectively.

Original languageEnglish (US)
Pages (from-to)658-670
Number of pages13
JournalAdvances in Database Technology - EDBT
Issue number3
StatePublished - Mar 20 2023
Event26th International Conference on Extending Database Technology, EDBT 2023 - Ioannina, Greece
Duration: Mar 28 2023Mar 31 2023

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Science Applications

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