December 10, 2018: Half-Day Tutorial

Tutorial I: Privacy-Aware Smart Metering

Organizers: Deniz Gunduz (Imperial College London, London, UK), Tobias Oechtering (KTH Royal Institute of Technology, Stockholm, Sweden)

Description:The electrical grid is a highly complex cyber physical system, vital for our society and economy. For efficient and reliable electrical energy generation and distribution, advanced control mechanisms have been introduced into the grid. These mechanisms help us monitor the grid in order to rapidly diagnose potential problems, and to dynamically react to variations in demand and supply. Smart electricity meters (SMs) are an essential component of the smart grid control mechanisms; they provide accurate sensing capabilities, and establish a two-way communication infrastructure. SMs also enable demand side management, potentially responding to real-time pricing, and exploiting the growing elasticity in energy consumption of users. SMs can be further linked with smart water and gas meters to better manage energy infrastructure for smarter/greener homes.

However, SM readings can also reveal users habits and behaviours since every electric appliance has its own detectable power consumption signature. Using these signatures together with non-intrusive load monitoring (NILM) techniques can easily lead to real-time surveillance of users activities. Hence, there is a significant tension between sharing data for the reliability and efficiency of the grid, and consumer privacy. Since SM readings are transmitted over communication networks shared by other users and applications, the system is vulnerable to attacks that may hack and manipulate SM readings of users and energy costs, fake power consumption data at a large scale to stress or crash the entire power network.

This half-day tutorial will provide a comprehensive overview of growing privacy threats to SMs and the smart grid in general. Potential attacks and their impact on the grid will be reviewed. We will then go over various techniques in the literature against the potential attacks. We will provide an overview of the tools that can be employed to guarantee that SMs can continue to play their essential role in enabling the future smart grid without threatening user privacy.

Tutorial Overview:

• Objectives

As the integration of renewables into the energy network increases, and storage devices, in the form of electric car batteries or elasticity of consumption, become widely available, the capability for real time control and monitoring at the customer end is becoming increasingly important as it provides the means to balance the variability and intermittency of the renewable resources. As a consequence, pricing and billing are becoming extremely dynamic and complex. The more distributed and the less deterministic the electricity network becomes, due to distributed renewable resources, the smarter the grid will need to be to ensure reliable operation. Utility providers need to gather higher resolution information in order to track the network state accurately, detect failures quickly, prevent energy theft, and foremost, increase the efficiency through real-time pricing and demand side management. Therefore, resolving the privacy concerns in smart grids is an urgent challenge for its successful development.

We will open the tutorial with an overview of the main objectives of SMs, and how these objectives lead to privacy threats. We will then review how the current SM systems function.

• Challenges and Tools

In addition to privacy threats from third party intruders, SMs present another intrinsic privacy threat, which is significantly more challenging compared to classical data privacy. SMs are installed specifically to provide finegrained information on the energy consumption of users and the state of the grid to the utility provider. Although the utility provider is the intended recipient of the SM readings, it is not fully trusted by the consumers. SM privacy constitutes even a greater risk for businesses, such as server farms, whose performance can be accurately tracked from their energy consumption profile. The SM infrastructure should be designed such that the information provided to the utility provider is good for control, state estimation, and billing purposes, yet it does not conflict with consumer privacy. This highly challenging task requires fundamental research in privacy assurance with applications to power systems.

The main goal of the tutorial will be to provide a comprehensive overview of these challenges, and present the existing techniques in the literature to tackle them. These will involve practical as well as more fundamental theoretical approaches.

• Concluding Remarks

Before concluding, we will provide a comparison of the different analytical approaches covered in the tutorial and discuss their implications on practical design. We close with a discussion on current and future challenges, in particular related to the design of privacy-aware smart grids.


Deniz Gunduz received the B.S. degree in electrical and electronics engineering from METU, Turkey in 2002, and the M.S. and Ph.D. degrees in electrical engineering from NYU Polytechnic School of Engineering (formerly Polytechnic University) in 2004 and 2007, respectively. After his PhD, he served as a postdoctoral research associate at Princeton University, and as a consulting assistant professor at Stanford University. He was a research associate at CTTC in Barcelona, Spain until September 2012, when he joined the Electrical and Electronic Engineering Department of Imperial College London, UK, where he is currently a Reader (Associate Professor) in information theory and communications, and leading the Information Processing and Communications Lab.

His research interests lie in the areas of communications and information theory, machine learning, and security and privacy in cyber-physical systems. Dr. Gndz is an Editor of the IEEE TRANSACTIONS ON COMMUNICATIONS, and the IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING. He is the recipient of the Early Achievement Award of the IEEE Communications Society Communication Theory Technical Committee in 2017, a Starting Grant of the European Research Council (ERC) in 2016, and the IEEE Communications Society Best Young Researcher Award for the Europe, Middle East, and Africa Region in 2014. He also received the Best Paper Award at the 2016 IEEE Wireless Communications and Networking Conference (WCNC), and the Best Student Paper Awards at the 2018 IEEE Wireless Communications and Networking Conference (WCNC) and 2007 IEEE International Symposium on Information Theory (ISIT). He is the General Co-chair of the 2018 Workshop on Smart Antennas, and previously served as the General Co-chair of the 2016 IEEE Information Theory Workshop.

Selected Publications:

  • G. Giaconi, D. Gunduz and H. V. Poor, Privacy-aware smart metering: Progress and challenge, IEEE Signal Processing Magazine, to appear.
  • G. Giaconi, D. Gunduz and H. V. Poor, Smart meter privacy with renewable energy and a storage device, IEEE Transactions on Information Forensics & Security, vol. 13, no. 1, pp. 129 - 142, Jan. 2018.
  • J. Gomez-Vilardebo and D. Gunduz, Smart meter privacy for multiple users in the presence of an alternative energy source, IEEE Trans. on Information Forensics & Security, vol. 10, no. 1, pp. 132-141, Jan. 2015.
  • O. Tan, D. Gunduz and H. V. Poor, Increasing smart meter privacy through energy harvesting and storage devices, IEEE Journal on Selected Areas in Communications: Smart Grid Communications, vol. 7, no. 31, pp. 1331-1341, Jul. 2013.

Tobias J. Oechtering received the Dipl.-Ing. degree in electrical engineering and information technology from RWTH Aachen University, Germany, in 2002, the Dr.-Ing. degree in electrical engineering from the Technische Universitt Berlin, Germany, in 2007, and the Docent degree in communication theory from KTH Royal Institute of Technology in 2012. After his graduation he joined the Fraunhofer Heinrich-Hertz Institute as a postdoctoral research for one year. In 2008, he joined the Communication Theory Lab, KTH Royal Institute of Technology, Stockholm, Sweden, as an ACCESS Post-doc. Between 2010 and 2013 he had a tenure track Assistant Professor position at the same department and has been an Associate Professor since 2013.

His research interests include statistical signal processing, physical layer privacy and security, networked control, and communication and information theory. Dr. Oechtering was an Editor of the IEEE COMMUNICATIONS LETTERS from 2012 to 2015. He is currently an Associate Editor of the IEEE TRANSACTIONS ON INFORMATION FORENSIC AND SECURITY since 2016 and member of the editorial board of the MDPI Entropy journal. He was recipient of the IEEE SPAWC 2017 Best Student Paper award, the Frderpreis 2009 Award from the Vodafone Foundation, and the 2010 ChinaCom Best Paper Award. He received from the Swedish government funded strategic research area ICT-The Next Generation in 2010 a starting grant and in 2016 a collaborative consolidator project grant, which he leads. Since 2015, he is coordinator of the CHIST-ERA project COPES COnsumer-centric Privacy in smart Energy gridS with KTH, Imperial, ETH Zurich, and INRIA as partners. In 2016 he also received two PhD student project grants on Energy STOrage for smart Meter Privacy funded by the Swedish Energy Agency and KTH graduate school on Digitalisation of Swedish EE industry.

Selected Publications:

  • Z. Li, T. J. Oechtering, and D. Gunduz “Smart Meter Privacy Based on Adversarial Hypothesis Testing,â€?2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, June, 2017.
  • Z. Li, T. J. Oechtering, and M. Skoglund, “Privacy-preserving Energy Flow Control in Smart Grids,â€?in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016.
  • Z. Li, and T. J. Oechtering, “Privacy on Hypothesis Testing in Smart Grids,â€?in Proc. IEEE Information Theory Workshop ITW â€?5, Jeju, South Korea, Oct. 2015.

December 10, 2018: Half-Day Tutorial

Tutorial II: Privacy- Location Privacy: Threats and Opportunities

Organizers: Fernando Pérez-González (University of Vigo, Spain), Simón Oya (University of Vigo, Spain)

Description:The Location based services have a pervasive presence in our society. From the usage of the phone to find nearby places to the simple act of connecting to the phone network, there is a vast amount of personal location information being collected by different service providers. Why do we like location-based apps? How easy can we be geolocated? Why is it dangerous? Can we protect ourselves against geolocation threats?

In this tutorial, we will provide an overview of location privacy technologies, answering these questions and giving a complete view of the state-of-the-art and the challenges ahead. We will revisit the techniques that can be used to find out our location, ranging from triangulation using phone signals to inference using information from social networks, and provide some real examples of location privacy exposures. We will give an overview of location privacy preserving mechanisms proposed in the last decade, including hiding, cloaking, obfuscation and dummy-based methods, as well as anonymization techniques. We will explain the traditional approach that has been followed by the scientific community to quantify location privacy and design protection mechanisms, as well as the more recent findings that challenge this approach. We will see that location privacy technologies stand at the crossroads of several disciplines, such as signal processing, information theory, estimation theory, and cryptography, and thus can benefit significantly from contributions from different research areas. Finally, we will provide an overview of the current practical solutions to the location privacy problem and discuss the challenges ahead.

List of Topics:

The topics of the tutorial include:

• Introduction to the location privacy threat (30 mins).

• Overview of Location Privacy-Preserving Mechanisms (30 mins):

  • Hiding-based mechanisms.
  • Cloaking-based mechanisms.
  • Obfuscation-based mechanisms.
  • Dummy-based mechanisms.
  • Anonymity techniques

• Quantifying privacy and utility (45 mins):

  • Traditional approach: adversary error versus average quality loss.
  • Information-theoretic metrics of privacy.
  • Geo-indistinguishability.
  • Application-tailored metrics.

• Location Privacy-Preserving Mechanism design (45 mins):

  • LPPM design framework.
  • Traditional mechanism design.
  • Multi-dimensional notion of privacy and utility.
  • Mobility model selection: theory vs. practice.

• Overview of existing solutions and challenges ahead (30 mins).

Target Participants:

Location privacy technologies offer interesting opportunities to researchers of different areas of expertise. This tutorial targets experts in a wide variety of fields, including: signal processing, information theory, estimation theory, game theory, software engineering and database management.

Prerequisites for Participants:

The tutorial is designed to be an introduction to the location privacy problem, and to present the current challenges and opportunities in the field. Therefore, it will be accessible and understandable to the general audience of WIFS. The prerequisites are:

- Basic knowledge of probability theory and statistics

- Basic knowledge of information-theory


Fernando Pérez-González received his Ph.D. in Telecommunication Engineering from the University of Vigo, Spain, in 1993. He is Professor at the Signal Theory and Communications Department, University of Vigo since 2000, where he leads the Signal Processing and Communications Group (GPSC). In 2007-2014 he was the founding Executive Director of the Galician Research and Development Center in Advanced Telecommunications (GRADIANT), a semi-private research center. From 2009-2012 he was the holder of the prestigious Prince of Asturias Endowed Chair on Information Science and Technology at the University of New Mexico (UNM). His research interests lie in the crossroads of signal processing, security/privacy and communications, in particular, those problems in which an adversary is present. Fernando has co-authored more than 230 peer-reviewed journal and conference papers, 15 international patents, and has participated in 5 European projects related to media security and data privacy.

He has given tutorials and keynotes in international conferences. He has served in the Editorial Board of several international journals, including IEEE Trans. on Information Forensics and Security and IEEE Signal Processing Letters. He is the current Editor in Chief of EURASIP Journal on Information Security.

Fernando is a member of the Galician Royal Academy of Sciences and an IEEE Fellow.

Simon Oya received the Telecommunication Engineer degree from the University of Vigo, Spain,in 2012. He is currently pursuing the Ph.D. degree in Information and Communication Technologies in the same university, which he expects to complete in October 2018.

His research interest is the study of privacy-preserving technologies from a signal processing point of view, focusing on anonymous communication channels and location privacy. Simon has co-authored 2 journal papers (IEEE TIFS and IEEE/ACM TNET) and 5 conference papers, including a location privacy publication in CCS�7.

December 10, 2018: Half-Day Tutorial

Tutorial III: Role of Adversaries in Deep Learning

link to presentation

Organizers: Mayank Vatsa (IIIT Delhi, India), Richa Singh (IIIT Delhi, India), Nalini Ratha (IBM T. J. Watson Research Center, USA)

Description:The Deep neural network architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. However, adversarial attacks on automated classification systems has been an area of interest for a long time. In 2002, Ratha et al. proposed eight points of attacks on a biometric system and several of these attacks are relevant for non-biometric classification tasks as well, including object recognition and autonomous driving. For instance, the adversary can operate at the input level or the decision level, and lead to incorrect prediction results by the classifier. Therefore, it is important to detect the adversarial perturbations and mitigate the effect caused due to such adversaries.

The research on adversarial learning has three key components: (i) creating adversarial images, (ii) detecting whether an image is adversely altered or not, and (iii) mitigating the effect of the adversarial perturbation process. These adversaries create different kinds of effect on the input and detecting them requires the application of a combination of hand-crafted as well as learnt features; for instance, some of the existing attacks can be detected using principal components while some hand-crafted attacks can be detected using well defined image processing operations. This tutorial will focus on these three key ideas related to adversarial learning (aka perturbations, detection, and mitigation), building from basics of neural network, deep learning, adversarial learning to discussing new algorithms for detection and mitigation, and conclude with some of the research questions in this spectrum.

Target audience and Prerequisite:

This tutorial will be relevant to researchers and graduate students working in adversarial pattern recognition and deep learning. It is expected that the attendees will have fundamental knowledge of machine learning.

Outline of Tutorial:

The tutorial is planned to be organized under following broad sections and the duration will be around 4 hours.

  • Introduction to Neural Networks
  • Deep learning architectures
  • What is Adversarial Perturbation?
  • How adversarial perturbations affect deep learning based recognition algorithms?
  • Simple algorithm for creating adversarial perturbations
  • Popular algorithms for adversarial pattern perturbations
  • How to detect perturbations?
  • Popular algorithms for adversarial perturbation detection in deep learning framework
  • What is Mitigation wrt Adversarial Perturbation?
  • Algorithms for mitigating the effect of perturbation
  • Future research ideas


Mayank Vatsa received the M.S. and Ph.D. degrees in computer science from West Virginia University, Morgantown, USA, in 2005 and 2008, respectively. He is currently an Associate Professor with the IIIT Delhi, India and Adjunct Associate Professor at West Virginia University, USA. He is also the Head for the Infosys Center on Artificial Intelligence at IIIT Delhi. His research has been funded by UIDAI and DeitY Government of India. He has authored over 200 publications in refereed journals, book chapters, and conferences. His areas of interest are biometrics, image processing, computer vision, and information fusion. He is a recipient of the AR Krishnaswamy Faculty Research Fellowship, the FAST Award by DST, India, and several best paper and best poster awards in international conferences. He is also the Vice President (Publications) of IEEE Biometrics Council, an Associate Editor of the IEEE ACCESS, and an Area Editor of Information Fusion (Elsevier). He served as the PC Co-Chair of ICB 2013, IJCB 2014, and ISBA 2017.

Richa Singh is currently an Associate Professor with the IIIT Delhi, India and an Adjunct Associate Professor with the West Virginia University, USA. Her areas of interest are biometrics, pattern recognition, and machine learning. She is a recipient of the Kusum and Mohandas Pai Faculty Research Fellowship at the Indraprastha Institute of Information Technology, the FAST Award by DST, India, and several best paper and best poster awards in international conferences. She is also an Editorial Board Member of Information Fusion (Elsevier), and Associate Editor of Computer Vision and Image Understanding, Pattern Recognition, IEEE Access and the EURASIP Journal on Image and Video Processing (Springer). She has served as the General Co-Chair of IEEE ISBA 2017 and PC Co-Chair of IEEE BTAS 2016. She is currently serving as the PC Co-Chair of IWBF 2018 and IEEE AFRG 2019.

Nalini Ratha is a Research Staff Member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY where he is the team leader for the biometrics-based authentication research. He has over 20 years of experience in the industry working in the area of pattern recognition, computer vision and image processing. He received his B. Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur, M.Tech. degree in Computer Science and Engineering also from Indian Institute of Technology, Kanpur and Ph.D. in Computer Science from Michigan State University. He has authored more than 80 research papers in the area of biometrics and has been co-chair of several leading biometrics conferences and served on the editorial boards of IEEE Trans. on PAMI, IEEE Trans. on SMC-B, IEEE Trans. on Image Processing and Pattern Recognition journal. He has co-authored a popular book on biometrics entitled “Guide to Biometrics�and also co-edited two books entitled “Automatic Fingerprint Recognition Systems�and “Advances in Biometrics: Sensors, Algorithms and Systems� He has offered tutorials on biometrics technology at leading IEEE conferences and also teaches courses on biometrics and security. He is Fellow of IEEE, Fellow of IAPR and a senior member for ACM. His research interests include biometrics, pattern recognition and computer vision. He has been an adjunct professor at IIIT Delhi, Cooper Union and NYU-Poly. During 2011-2012 he was the president of the IEEE Biometrics Council. At IBM, he has received several awards including a Research Division Award, Outstanding Innovation Award and Outstanding Technical Accomplishment Award along with several patent achievement awards.

Relevant References from Presenters::

  • S. Chhabra, R. Singh, M. Vatsa, G. Gupta, Anonymizing k Facial Attributes via Adversarial Perturbations, In Proceedings of International Joint Conference on Artificial Intelligence, 2018.
  • G. Goswami, N. Ratha, A. Agrawal, R. Singh, and M. Vatsa, Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks, In Proceedings of Association for the Advancement of Artificial Intelligence, 2018
  • I. Manjani, S. Tariyal, M. Vatsa, R. Singh, A. Majumdar, Detecting Silicone Mask based Presentation Attack via Deep Dictionary Learning, IEEE Transactions on Information Forensics and Security, Volume 12, No. 7, pp. 1713-1723, 2017.
  • A. Agarwal, R. Singh, M. Vatsa, and A. Noore, SWAPPED! Digital Face Presentation Attack Detection via Weighted Local Magnitude Pattern, In Proceedings of IEEE International Joint Conference on Biometrics, 2017.
  • A. A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, Face Presentation Attack with Latex Masks in Multispectral Videos, In Proceedings of IEEE Computer Vision and Pattern Recognition Workshops on Perception Beyond the Visible Spectrum, 2017.
  • A. Agarwal, R. Singh, and M. Vatsa, Face Anti-spoofing using Haralick Features, In Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems, 2016.
  • S. Bharadwaj, T. I. Dhamecha, M. Vatsa and R. Singh, "Computationally Efficient Face Spoofing Detection with Motion Magnification," In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 105-110, 2013.
  • N. K. Ratha, S. Chikkerur, J. H. Connell and R. M. Bolle, "Generating Cancelable Fingerprint Templates," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 561-572, April 2007.
  • N. K. Ratha, J. Connell, R. M. Bolle and S. Chikkerur, "Cancelable Biometrics: A Case Study in Fingerprints," 18th International Conference on Pattern Recognition, Hong Kong, pp. 370-373, 2006.
  • R. M. Bolle, J.H. Connell, N.K. Ratha, Biometric perils and patches, Pattern Recognition, Volume 35, Issue 12, Pages 2727-2738, 2002.

December 10, 2018: Half-Day Tutorial

Tutorial IV: Biometric Template Protection and Evaluation

Organizers: Marta Gomez-Barrero

Description:The recent discussion on privacy protection, for instance within the framework of the new EU General Data Protection Regulation (GDPR), has raised privacy concerns regarding the storage and use of biometric data. The international standard ISO/IEC 24745 has established two main requirements for protecting biometric templates i) irreversibility and ii) unlinkability back in 2011. Since then, numerous efforts have been directed to the development and analysis of irreversible templates. However, only very recently, in 2018, a systematic quantitative manner to analyse the unlinkability of such templates was proposed. As a consequence, the lack of a unified framework to analyse all privacy aspects of biometric template protection schemes may have hindered their further deployment.

In this tutorial, we will focus on the objective and quantitative evaluation of biometric template protection schemes. To that end, the main concepts related to template protection will be introduced, together with a review of the main existing approaches. A unified framework for the evaluation and benchmarking will be subsequently described, including the recently proposed unlinkability metric. It should be noted that these metrics can be also applied in other areas of signal processing in the encrypted domain. Finally, we will assess the potential from this approach by evaluating the performance of two state-the-art techniques for biometric template protection: Bloom filters and Homomorphic Encryption.

Target Participants:

Anyone interested in privacy related concerns or privacy protection during the biometric recognition or signal processing in general.

Prerequisites for Participants:

Knowledge about general / basic concepts on biometrics, pattern recognition and e-security.

List of topics

1. General concepts on biometric template protection. What does privacy protection mean for biometrics? Classification of existing approaches, including cancelable biometrics, biometric cryptosystems and homomorphic encryption.

  • ISO/IEC 24745 on Biometric information protection
  • C. Rathgeb, A. Uhl, "A Survey on Biometric Cryptosystems and Cancelable Biometrics", EURASIP Journal on Information Security, 2011
  • C. Aguilar-Melchor, S. Fau, C. Fontaine, G. Gogniat, R. Sirdey, "Recent advances in homomorphic encryption: A possible future for signal processing in the encrypted domain" IEEE Signal Processing Magazine, vol. 30, no. 2, pp. 108-117, 2013
  • V. M. Patel, N. K. Ratha, R. Chellappa, "Cancelable biometrics: A review", IEEE Signal Processing Magazine, vol. 32, no. 5, pp. 54-65, 2015

2. Benchmarking biometric template protection schemes. How to evaluate and compare different systems in terms of the ISO/IEC 24745 requirements: accuracy degradation, irreversibility, unlinkability.

  • S. Rane, "Standardization of biometric template protection", IEEE MultiMedia, vol. 21, no. 4, pp. 94-99, 2014
  • M. Gomez-Barrero, J. Galbally, C. Rathgeb, C. Busch, "General Framework to Evaluate Unlinkability in Biometric Template Protection Systems" IEEE Trans. on Information Forensics and Security, vol. 3, no. 6, pp. 1406-1420, June 2018

3. Case study: how to evaluate and compare two different biometric template protection schemes based on Bloom Filter and on Homomorphic Encryption

  • M. Gomez-Barrero, C. Rathgeb, G. Li, R. Raghavendra, J. Galbally, C. Busch, "Multi-Biometric Template Protection Based on Bloom Filters", Information Fusion, vol. 42, pp. 37-50, July 2018
  • M. Gomez-Barrero, E. Maiorana, J. Galbally, P. Campisi, J. Fierrez, "Multi-Biometric Template Protection Based on Homomorphic Encryption" Pattern Recognition, vol. 67, pp. 149-163, July 2017

4. Discussion and lessons learned: This part will initiate open discussion on the biometric template protection and methods for the evaluation of such protection. We will also discuss on the lessons learned from this line of research and on the need for further work in this area.


Marta Gomez-Barrero received her MSc degrees in Computer Science and Mathematics, and her PhD degree in Electrical Engineering, from Universidad Autonoma de Madrid, in 2011 and 2016, respectively. Since 2016 she is a PostDoctoral researcher at the Center for Research in Security and Privacy (CRISP), Germany. Her current research focuses on the development of privacy-enhancing biometric technologies as well as Presentation Attack Detection methods, within the wider fields of pattern recognition and machine learning. She is the recipient of a number of distinctions, including: EAB European Biometric Industry Award 2015, Siew-Sngiem Best Paper Award at ICB 2015, Archimedes Award for young researches from Spanish Ministry of Education in 2013 and Best Poster Award at ICB 2013.

Relevant References from Presenter:

  • M. Gomez-Barrero, C. Rathgeb, G. Li, R. Raghavendra, J. Galbally, C. Busch, "Multi-Biometric Template Protection Based on Bloom Filters", Information Fusion, vol. 42, pp. 37-50, July 2018
  • M. Gomez-Barrero, J. Galbally, C. Rathgeb, C. Busch, "General Framework to Evaluate Unlinkability in Biometric Template Protection Systems", IEEE Trans. on Information Forensics and Security, vol. 3, no. 6, pp. 1406-1420, June 2018
  • M. Gomez-Barrero, J. Galbally, A. Morales, J. Fierrez, "Privacy-Preserving Comparison of Variable-Length Data with Application to Biometric Template Protection", IEEE Access, vol. 5 (1), pp. 8606-8619, Dec. 2017
  • M. Gomez-Barrero, E. Maiorana, J. Galbally, P. Campisi, J. Fierrez, "Multi-Biometric Template Protection Based on Homomorphic Encryption", Pattern Recognition, vol. 67, pp. 149-163, Jul. 2017.
  • E. Martiri, M. Gomez-Barrero, B. Yang, C. Busch, "Biometric Template Protection Based on Bloom Filters and Honey Templates", IET Biometrics, Vol. 6 (1), pp. 19-26, Jan. 2017.
  • M. Gomez-Barrero, C. Rathgeb, J. Galbally, C. Busch, J. Fierrez, "Unlinkable and irreversible biometric template protection based on Bloom filters", Information Sciences, vol. 370-371, pp. 18-32, Nov. 2016.
  • C. Rathgeb, M. Gomez-Barrero, C. Busch, J. Galbally and J. Fierrez, "Towards Cancelable Multi-Biometrics based on Adaptive Bloom Filters: A Case Study on Feature Level Fusion of Face and Iris", Proc. Int. Workshop on Biometrics and Forensics, IWBF, Gjøvik, Norway, Mar. 2015.
  • M. Gomez-Barrero, C. Rathgeb, J. Galbally, J. Fierrez and C. Busch, "Protected Facial Biometric Templates Based on Local Gabor Patterns and Adaptive Bloom Filters", Proc. IAPR/IEEE Int. Conf. on Pattern Recognition, ICPR, pp. 4483-4488, Stockholm, Sweden, Aug. 2014.

December 10, 2018: Half-Day Tutorial

Tutorial V: TBD