Secure multi-party computation for supply chain collaboration

Despite evidence that collaborating in the supply chain can reduce inefficiency and result in mutual gain, parties do not wish to collaborate if they have to share their private proprietary information. The main reason for their privacy concern is that the party does not want to lose their competitive advantage by giving away company secrets....

Secure signal processing: Privacy preserving cryptographic protocols for multimedia

Recent advances in technology provided a suitable environment for the people in which they can benefit from online services in their daily lives. Despite several advantages, online services also constitute serious privacy risks for their users as the main input to algorithms are privacy sensitive such as demographic information, shopping...

Mitigating Inference Attacks in Collaborative Credit Card Fraud Detection using Secure Multi-Party Selection

The convenient service offered by credit cards and the technological advances in e-commerce have caused the number of online payment transactions to increase daily. With this rising number, the opportunity for fraudsters to obtain cardholder details via online credit card fraud has also increased. As a result, according to the European Central...

Collaborative Private Decision-Tree Evaluation using (Multi-Key) Fully Homomorphic Encryption: with applications to Risk-Adaptive Access Control

Decision-tree evaluation is a widely-used classification approach known for its simplicity and effectiveness. Decision-tree models are shown to be helpful in classifying instances of fraud, malware, or diseases and can be used to make dynamic, flexible access decisions within an access-control system. These applications often require sensitive...

Scenario Analysis of Secure Multi-party Computation implementation in EU-based multinational banks

EU banks have a dual obligation to generate and protect assets as a due diligence for their customers, their business longevity, and in accordance with regulatory bodies. The effective use of data presents opportunities for banks to meet their obligations in improving financial risk and also ensuring business growth and continuity in their...

FLVoogd: Robust And Privacy Preserving Federated Learning

In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring...

Realising platform control in data marketplaces through Secure Multi-Party Computation: A qualitative study exploring the use of Secure Multi-Party Computation (MPC) as an instrument for realising platform control in data marketplaces

Practical problem: In today's digitally transformed and connected world, data has become a critical strategic corporate resource. In this context, data marketplaces are becoming more popular since they enable wider accessibility and more efficient interaction among companies. Despite this, there are several barriers in sharing data through this...

Share your Secrets for Private Forecasting with Vertical Federated Learning

Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training...

Confidentiality-Preserving Collaborative Bayesian Networks

Effective large-scale process optimization in manufacturing industries requires close cooperation between different parties of human experts who encode their knowledge of related domains as Bayesian network models. For example, parties in the steel industry must collaboratively use their Bayesian networks on process parameters at the maker,...