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Found 23 papers, 2 papers with code
Private Text Classification with Convolutional Neural Networks
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches.
Privacy-Preserving Fair Item Ranking
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis.
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education.
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance.
Training Differentially Private Models with Secure Multiparty Computation
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data.
Privacy-Preserving Training of Tree Ensembles over Continuous Data
In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (``extra-trees'') on the original data.
Privacy-Preserving Feature Selection with Secure Multiparty Computation
In this work, we propose the first MPC based protocol for private feature selection based on the filter method, which is independent of model training, and can be used in combination with any MPC protocol to rank features.
Privacy-Preserving Video Classification with Convolutional Neural Networks
We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks that allows a party to infer a label from a video without necessitating the video owner to disclose their video to other entities in an unencrypted manner.
Private Speech Classification with Secure Multiparty Computation
We evaluate the efficiency-security-accuracy trade-off of the proposed solution in a use case for privacy-preserving emotion detection from speech with a convolutional neural network.
Inline Detection of DGA Domains Using Side Information
We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function.
User Profiling Using Hinge-loss Markov Random Fields
In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few.
Protecting Privacy of Users in Brain-Computer Interface Applications
Machine learning (ML) is revolutionizing research and industry.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few.
CharBot: A Simple and Effective Method for Evading DGA Classifiers
In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM. MI (a deep learning approach).
VirtualIdentity: Privacy-Preserving User Profiling
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies.
Character Level Based Detection of DGA Domain Names
Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification.
Solving stable matching problems using answer set programming
Since the introduction of the stable marriage problem (SMP) by Gale and Shapley (1962), several variants and extensions have been investigated.
Characterizing and Extending Answer Set Semantics using Possibility Theory
In particular, while the complexity of most reasoning tasks coincides with standard disjunctive ASP, we find that brave reasoning for programs with weak disjunctions is easier.
Modeling Stable Matching Problems with Answer Set Programming
Our encoding can easily be extended and adapted to the needs of specific applications.
Discovering Missing Wikipedia Inter-language Links by means of Cross-lingual Word Sense Disambiguation
The input for the inter-language link detection system is a set of Dutch pages for a given ambiguous noun and the output of the system is a set of links to the corresponding pages in three target languages (viz.
