Onur Günlü

Prof. Dr. Onur Günlü

Lehrstuhl für Nachrichtentechnik, TU Dortmund University, Dortmund, North Rhine-Westphalia, Germany
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Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 March 2021 in IEEE Journal on Selected Areas in Information Theory
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Journal Article
Closed access
MBMatthieu Bloch
Matthieu Bloch
Onur GünlüOnur Günlü
Onur Günlü
AYAylin Yener
Aylin Yener
FOFrederique Oggier
Frederique Oggier
H. Vincent PoorH. Vincent Poor
H. Vincent Poor
RSRafael F. Schaefer
Rafael F. Schaefer
This tutorial reviews fundamental contributions to information security. An integrative viewpoint is taken that explains the security metrics, including secrecy, privacy, and others, the methodology of information-theoretic approaches, along with the arising system design principles, as well as techniques that enable the information-theoretic designs to be applied in real communication and computing systems. The tutorial, while summarizing these contributions, argues for the simultaneous pivotal role of fundamental limits and coding techniques for secure communication system design.
6 June 2021
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Conference Paper
Closed access
MKMuah Kim
Muah Kim
Onur GünlüOnur Günlü
Onur Günlü
RSRafael F. Schaefer
Rafael F. Schaefer
Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.
17 August 2021 in PLOS ONE
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Journal Article
Open access
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EBEfe Bozkir
Efe Bozkir
Onur GünlüOnur Günlü
Onur Günlü
WFWolfgang Fuhl
Wolfgang Fuhl
RSRafael F. Schaefer
Rafael F. Schaefer
EKEnkelejda Kasneci
Enkelejda Kasneci
New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.
10 May 2023 in IEEE Journal on Selected Areas in Information Theory
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Journal Article
Closed access
Onur GünlüOnur Günlü
Onur Günlü
MBMatthieu Bloch
Matthieu Bloch
RSRafael F. Schaefer
Rafael F. Schaefer
AYAylin Yener
Aylin Yener
This work considers the problem of mitigating information leakage between communication and sensing in systems jointly performing both operations. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to convey information, while simultaneously performing channel state estimation; one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to part of the transmitted information. The model abstracts the challenges behind security for joint communication and sensing if one views the channel state as a key attribute, e.g., location. For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed. The characterization is exact when the broadcast channel is either physically-degraded or reversely-physically-degraded. The partial characterization is also extended to the situation in which the entire transmitted message should be kept secret. The benefits of a joint approach compared to separation-based secure communication and state-sensing methods are illustrated with binary joint communication and sensing models.
7 May 2018 in IEEE Transactions on Information Forensics and Security
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Journal Article
Closed access
Onur GünlüOnur Günlü
Onur Günlü
Gerhard KramerGerhard Kramer
Gerhard Kramer
The key-leakage-storage region is derived for a generalization of a classic two-terminal key agreement model. The additions to the model are that the encoder observes a hidden, or noisy, version of the identifier, and that the encoder and decoder can perform multiple measurements. To illustrate the behavior of the region, the theory is applied to binary identifiers and noise modeled via binary symmetric channels. In particular, the key-leakage-storage region is simplified by applying Mrs. Gerber's lemma twice in different directions to a Markov chain. The growth in the region as the number of measurements increases is quantified. The amount by which the privacy-leakage rate reduces for a hidden identifier as compared to a noise-free (visible) identifier at the encoder is also given. If the encoder incorrectly models the source as visible, it is shown that substantial secrecy leakage may occur and the reliability of the reconstructed key might decrease.
3 May 2018 in Entropy
Verified
Journal Article
Open access
PDF file
Onur GünlüOnur Günlü
Onur Günlü
TKTasnad Kernetzky
Tasnad Kernetzky
OİOnurcan İşcan
Onurcan İşcan
Vladimir SidorenkoVladimir Sidorenko
Vladimir Sidorenko
Gerhard KramerGerhard Kramer
Gerhard Kramer
RSRafael F. Schaefer
Rafael F. Schaefer
Different transforms used in binding a secret key to correlated physical-identifier outputs are compared. Decorrelation efficiency is the metric used to determine transforms that give highly-uncorrelated outputs. Scalar quantizers are applied to transform outputs to extract uniformly distributed bit sequences to which secret keys are bound. A set of transforms that perform well in terms of the decorrelation efficiency is applied to ring oscillator (RO) outputs to improve the uniqueness and reliability of extracted bit sequences, to reduce the hardware area and information leakage about the key and RO outputs, and to maximize the secret-key length. Low-complexity error-correction codes are proposed to illustrate two complete key-binding systems with perfect secrecy, and better secret-key and privacy-leakage rates than existing methods. A reference hardware implementation is also provided to demonstrate that the transform-coding approach occupies a small hardware area.