Floragasse 7 – 5th floor, 1040 Vienna

MLDM

Machine Learning (ML) offers exciting possibilities for innovative products and improvements of existing services. To avoid negative consequences, such as the loss of costumer data or commercial secrets, it is important to consider security and privacy aspects before applying Machine Learning in real-world applications.

SBA Research conducts research in the area of privacy-preserving Machine Learning and develops novel solutions to mitigate related threats. In addition, we offer trainings and expert discussions on implications and available defense mechanisms as well as consulting services in the area of secure and privacy-preserving data management and ML.

Topics include:

  • Protection against theft of intellectual property (data or trained ML models)
  • Defense mechanisms against adversarial attacks
  • Privacy-preserving computation methods like federated learning and secure multi-party computation
  • Novel methods for data anonymization, including complex data types


Research

Machine Learning (ML) offers exciting possibilities for innovative products and improvements of existing services. To avoid negative consequences, such as the loss of costumer data or commercial secrets, it is important to consider security and privacy aspects before applying Machine Learning in real-world applications.

SBA Research conducts research in the area of privacy-preserving Machine Learning and develops novel solutions to mitigate related threats. In addition, we offer trainings and expert discussions on implications and available defense mechanisms as well as consulting services in the area of secure and privacy-preserving data management and ML.

Topics include:

  • Protection against theft of intellectual property (data or trained ML models)
  • Defense mechanisms against adversarial attacks
  • Privacy-preserving computation methods like federated learning and secure multi-party computation
  • Novel methods for data anonymization, including complex data types

With regards to data processing, preserving the privacy of individuals and protecting business secrets is highly relevant for organizations which are working with sensitive and/or personal data. In particular, if companies outsource ML models to external (cloud) providers to analyze their data, they have to consider privacy-preserving data analysis. Data anonymization or -synthetization are possible solutions for privacy protection. A further threat that has to be considered is an adversary recovering training data directly from ML models. SBA Research addresses how organizations can collaboratively build ML models while not directly sharing their data and guaranteeing privacy for their customers.

Automated decision making can have a significant influence on individuals and groups; hence, the robustness of the respective algorithms is of great concern when deploying such systems in real-world applications. Various types of attacks can trick a ML system into making wrong predictions. Backdoor attacks, for instance, poison the training data by injecting carefully designed (adversarial) samples to compromise the whole learning process. In this manner it is possible to, for example: cause classification errors in traffic sign recognition with safety critical implications on autonomous driving; evade spam filters; manipulate predictive maintenance; or circumvent face recognition systems.

Developing methods to detect and defend against these attacks is an important research topic for SBA Research.


Downloads

The Machine Learning and Data Management Research Group participates in the following research projects:

FeatureCloud

Privacy Preserving Federated Machine Learning and Blockchaining for Reduced Cyber Risks in a World of Distributed Healthcare

The FeatureCloud project develops privacy preserving federated machine learning mechanisms, especially for health-care related settings. Read More

PRIMAL

Privacy Preserving Machine Learning for Industrial Applications

PRIMAL will enable industrial deep learning applications by increasing the amount of… Read More

WellFort

WellFort

The WellFort project aims to research the basic mechanisms to provide secure storage for… Read More

GASTRIC

Gene Anonymisation and Synthetisation for Privacy

GASTRIC will investigate three aspects of microbiome sharing and analysis. First, an analysis on privacy… Read More

KnoP-2D

Evolving and Securing of Knowledge, Tasks and Processes in Distributed Dynamic Environments via a 2D-Knowledge/Process Graph

Within this project, we develop mechanisms for process mining in a distributed setting, i.e. where… Read More

Find the full publications list here.

MLDM consists of experts in the areas of privacy-preserving computation, privacy-preserving data publishing, synthetic data, adversarial machine learning, secure learning, detection of and defenses against attacks (e.g., poisoning attacks, evasion attacks), watermarking and fingerprinting of data, and machine learning models.


is senior researcher at SBA Research and leads the Machine Learning and Data Management Research Group. He is a lecturer at TU Wien as well as University of Applied Sciences Technikum Wien.

was a FEMtech intern and researcher at SBA Research.

was a FEMtech intern and researcher at SBA Research.

is part of the Management Board, senior researcher and head of Software Development at SBA Research.

is senior researcher at SBA Research.

is senior researcher at SBA Research.

is researcher at SBA Research and and PhD student at TU Wien.

is researcher at SBA.

is researcher at SBA Research.

is researcher at SBA Research.

is FEMtech intern and researcher at SBA Research.

is FEMtech intern and researcher at SBA Research.

is FEMtech intern and researcher at SBA Research.

The following scientific partners and company partners are / have been working closely together with the Machine Learning and Data Management Research Group:


Teaching

The Machine Learning and Data Management Group is also very active in teaching in subjects in their domain at TU Wien. This includes for example the following courses, all at the level of master curricula:


Bachelor | Master | PhD - Theses Suervision

The MLDM Research Group is supervising Bachelor, Master and PhD theses in the following areas. For further details please contact team lead Rudolf Mayer directly.

Adversarial Machine Learning

A good overview talk (in German) into Adversarial Machine Learning is given by Konrad Rieck: “Sicherheitslücken in der künstlichen Intelligenz”

Adversarial Inputs (resp. robustness against adversarial inputs)

Backdoor (data poisoning) attacks & defenses

Membership inference attack

Other attacks, e.g.

Privacy-preserving Machine Learning / Data Mining

Privacy-preserving analysis of data is becoming more relevant with the increasing amount of personal data being gathered. Several different approaches aiming at this problem exist, e.g.:Privacy-preserving data publishing

Privacy-preserving data publishing

  • k-anonymity, l-diversity, etc.
  • Differential privacy, including local differential privacy
  • Synthetic data generation
  • Goal: evaluation of privacy protection, utility of the published data, novel attack mechanisms, application of differential privacy to machine learning models, …

Privacy-preserving computation

Watermarking / fingerprinting of datasets

  • Goal: evaluation of schemes for their robustness of attacks, vs. their data utility, e.g. measured by effectiveness in machine learning tasks

To contact the team, please reach out to the individual team members or to the team lead Rudolf Mayer.

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