Publications related to Record linkage

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Storage Management in Smart Data Lake

Anastasia Ailamaki, Haoqiong Bian, Bikash Chandra, Ioannis Mytilinis

Data lakes are complex ecosystems where heterogeneity prevails. Raw data of diverse formats are stored and processed, while long and expensive ETL processes are avoided. Apart from data heterogeneity, data lakes also entail hardware heterogeneity. Typical ...

2021

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate t ...

EPFL2021

Multi-Dimensional Explanation of Target Variables from Documents

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Cleaning Denial Constraint Violations through Relaxation

Anastasia Ailamaki, Manolis Karpathiotakis, Styliani Asimina Giannakopoulou

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...

ASSOC COMPUTING MACHINERY2020

Query-driven Repair of Functional Dependency Violations

Anastasia Ailamaki, Manolis Karpathiotakis, Styliani Asimina Giannakopoulou

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...

IEEE COMPUTER SOC2020

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, Damián Pascual Ortiz

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-thr ...

2020

Reconciling Matching Networks of Conceptual Models

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Zoltán Miklós

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls fo ...

2019

Record linkage

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Storage Management in Smart Data Lake

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Multi-Dimensional Explanation of Target Variables from Documents

Cleaning Denial Constraint Violations through Relaxation

Query-driven Repair of Functional Dependency Violations

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

Reconciling Matching Networks of Conceptual Models

Graph Chatbot

Chat with Graph Search

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Cleaning Denial Constraint Violations through Relaxation

Storage Management in Smart Data Lake

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Query-driven Repair of Functional Dependency Violations

Reconciling Matching Networks of Conceptual Models

Multi-Dimensional Explanation of Target Variables from Documents