Post-Processing for Utility Improvement under Personalized Local Differential Privacy

Parlak C., Ceylan D., Balioglu B. K., Khodaie A., Gursoy M. E.

16th ACM Conference on Data and Application Security and Privacy, CODASPY 2026, Frankfurt am Main, Germany, 23 - 25 June 2026, pp.230-242, (Full Text)

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/3800506.3803503
  • City: Frankfurt am Main
  • Country: Germany
  • Page Numbers: pp.230-242
  • Keywords: data privacy, local differential privacy, personalized privacy protection, post-processing, privacy-enhancing technologies
  • Yıldız Technical University Affiliated: Yes

Abstract

Local Differential Privacy (LDP) has become a widely adopted paradigm for collecting and analyzing sensitive data from user devices. Personalized LDP (PLDP) extends LDP by enabling users to operate under different privacy budgets, better reflecting users' diverse privacy preferences that arise in practice. While PLDP provides greater flexibility, it introduces new challenges for the data collector, particularly in how estimates obtained from users with different privacy budgets should be combined and post-processed to maximize utility. In particular, while post-processing methods have been explored in LDP, similar studies remain lacking for PLDP. In this paper, we present a systematic study of combination and post-processing methods under PLDP. We consider two combination strategies: Simple Averaging (SA) and Inverse Variance Weighting (IVW), as well as three end-To-end post-processing architectures (No-PP, Combine-First, PP-First) that differ in whether post-processing is applied before combination, after combination, or not at all. Through extensive experiments, we show that IVW consistently outperforms SA. We further demonstrate that applying post-processing at the group level before aggregation (PP-First) generally yields higher utility than alternative architectures, although the gap narrows when IVW is used. Our results also reveal that no single post-processing method is universally optimal under PLDP; however, normalization-based methods such as Norm-Sub and Norm-Mul provide strongest performance. Finally, we analyze the impact of population-level privacy preferences and show how the distribution of privacy budgets affects overall utility and user incentives. Together, our results and findings provide practical guidance for designing effective pipelines that improve utility under PLDP.