Trajectory Hiding and Sharing for Supply Chains with Differential Privacy

With the fast development of e-commerce, there is a higher demand for timely delivery. Logistic companies want to send receivers a more accurate arrival prediction to improve customer satisfaction and lower customer retention costs. One approach is to share (near) real-time location data with recipients, but this also introduces privacy and...

FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages...

PRIDE: A Privacy-Preserving Decentralised Key Management System

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC...

Multi-party private set intersection protocols for practical applications

Multi-Party Private Set Intersection (MPSI) is an attractive topic in research since a practical MPSI protocol can be deployed in several real-world scenarios, including but not limited to finding the common list of customers among several companies or privacy-preserving analyses of data from different stakeholders. Several solutions have...

A Privacy-Preserving Asynchronous Averaging Algorithm based on State Decomposition

Average consensus algorithms are used in many distributed systems such as distributed optimization, sensor fusion and the control of dynamic systems. Consensus algorithms converge through an explicit exchange of state variables. In some cases, however, the state variables are confidential. In this paper, a privacy-preserving asynchronous...

Privacy-Preserving Distributed Graph Filtering

With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools for processing sensitive data. As a step towards a solution, we...

Gradually typing strategies

The Stratego language supports program transformation by means of term rewriting with programmable rewriting strategies. Stratego's traversal primitives support concise definition of generic tree traversals. Stratego is a dynamically typed language because its features cannot be captured fully by a static type system. While dynamic typing makes...

PREDICT: Efficient Private Disease Susceptibility Testing in Direct-to-Consumer Model

Genome sequencing has rapidly advanced in the last decade, making it easier for anyone to obtain digital genomes at low costs from companies such as Helix, MyHeritage, and 23andMe. Companies now offer their services in a direct-to-consumer (DTC) model without the intervention of a medical institution. Thereby, providing people with direct...

Data masking for recommender systems: Prediction performance and rating hiding

Data science challenges allow companies, and other data holders, to collaborate with the wider research community. In the area of recommender systems, the potential of such challenges to move forward the state of the art is limited due to concerns about releasing user interaction data. This paper investigates the potential of privacy...

Authentic Learning to Better Prepare for Preservation Work

Cultural heritage preservation has developed from an insular vocation into a field of innovative scientific methodologies characterized by a holistic approach combining a range of scientific fields. Unfortunately, preservation education has not been able to keep pace with all these developments. In this paper authentic learning is analyzed as...

Improved multi-microphone noise reduction preserving binaural cues

We propose a new multi-microphone noise reduction technique for binaural cue preservation of the desired source and the interferers. This method is based on the linearly constrained minimum variance (LCMV) framework, where the constraints are used for the binaural cue preservation of the desired source and of multiple interferers. In this...

A performance -based method for granular based method for granular -paste mix design

In this paper a performance-based method for the design of granular-paste mixtures will be proposed. Focus will be on the selection and proportioning of constituents to produce a mixture with a pre-defined shape holding ability. Shape holding ability of mixtures will be characterized by the shape preservation factor SPF. This SPF shows the...

Formalization and data enrichment for automated evaluation of building pattern preservation

Automated evaluation of generalization output relies to a large extent on that requirements (e.g. specifications, constraints) being formalized in machine-readable formats. Previous studies suggest that the formalization and automated evaluation are relatively easier for legibility constraints (improve the readability of maps) than for...

A Symmetry and Dispersion-Relation Preserving High-Order Scheme for Aeroacoustics and Aerodynamics

A new high-order, finite-volume scheme is presented that preserves the symmetry property and the dispersion relation of the convective operator. The scheme is applied to large-eddy simulation of compressible, turbulent flow and to the solution of the linearized Euler equations for aeroacoustic applications. For large-eddy simulation, the...

Multigrid Methods for Partial Differential Equations Arising From Fluid Flow Problems

In the paper, we present multigrid methods for solving scalar conservation laws and convection diffusion equations that frequently arise from fluid flow problems. For scalar conservation laws, we present monotonicity preserving and total variation diminishing multigrid methods. We generalize the upwind-biased residual restriction and...