Noisy Deletion, Markov Codes and Deep Decoding

Mandal, Avijit; Chatterjee, Avhishek; Thangaraj, Andrew

doi:10.1109/ncc48643.2020.9056064

Cited by 2 publications

(1 citation statement)

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“…Since histograms naturally show up frequently in information theory, probability theory and statistics, this result could be of independent interest. In addition, our novel matching scheme in the independent repetition case can be directly converted to a decoding strategy for input-constrained noisy synchronization channels, a well-investigated model in the information theory literature [45]- [48].…”

Section: Introductionmentioning

confidence: 99%

Database Matching Under Noisy Synchronization Errors

Bakirtas¹,

Erkip²

2023

Preprint

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The re-identification or de-anonymization of users from anonymized data through matching with publiclyavailable correlated user data has raised privacy concerns, leading to the complementary measure of obfuscation in addition to anonymization. Recent research provides a fundamental understanding of the conditions under which privacy attacks, in the form of database matching, are successful in the presence of obfuscation. Motivated by synchronization errors stemming from the sampling of time-indexed databases, this paper presents a unified framework considering both obfuscation and synchronization errors and investigates the matching of databases under noisy entry repetitions. By investigating different structures for the repetition pattern, replica detection and seeded deletion detection algorithms are devised and sufficient and necessary conditions for successful matching are derived. Finally, the impacts of some variations of the underlying assumptions, such as adversarial deletion model, seedless database matching and zero-rate regime, on the results are discussed. Overall, our results provide insights into the privacy-preserving publication of anonymized and obfuscated time-indexed data as well as the closely-related problem of the capacity of synchronization channels.

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Section: Introductionmentioning

confidence: 99%