Nikolai J. Podlesny

How Data Anonymisation Techniques influence Disease Triage in Digital Health: A Study on Base Rate Neglect

Christoph Meinel, Nikolai J. Podlesny, Anne V.D.M. Kayem, 2019, PDH.

Identifying Data Exposure Across Distributed High-Dimensional Health Data Silos through Bayesian Networks Optimised by Multigrid and Manifold

Christoph Meinel, Nikolai J. Podlesny, Anne V.D.M. Kayem, 2019, 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

Towards Identifying De-anonymisation Risks in Distributed Health Data Silos

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2019, DEXA.

Attribute Compartmentation and Greedy UCC Discovery for High-Dimensional Data Anonymization

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2019, CODASPY.

Minimising Information Loss on Anonymised High Dimensional Data with Greedy In-Memory Processing

Anne V. D. M. Kayem, Matthias Uflacker, Nikolai J. Podlesny, 2018, DEXA.

A Review of Scaling Genome Sequencing Data Anonymisation

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2021, AINA.

GPU Accelerated Bayesian Inference for Quasi-Identifier Discovery in High-Dimensional Data

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2021, AINA.

A Parallel Quasi-identifier Discovery Scheme for Dependable Data Anonymisation

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2021, Trans. Large Scale Data Knowl. Centered Syst..

On Chameleon Pseudonymisation and Attribute Compartmentation-as-a-Service

Christoph Meinel, Anne V. D. M. Kayem, Nikolai J. Podlesny, 2021, SECRYPT.