Searched for: author%3A%22Slokom%2C+M.%22
(1 - 8 of 8)
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Slokom, M. (author)
In the field of machine learning (ML), the goal is to leverage algorithmic models to generate predictions, transforming raw input data into valuable insights. However, the ML pipeline, consisting of input data, models, and output data, is susceptible to various vulnerabilities and attacks. These attacks include re-identification, attribute...
doctoral thesis 2024
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Slokom, M. (author), de Wolf, Peter Paul (author), Larson, M.A. (author)
We investigate an attack on a machine learning classifier that predicts the propensity of a person or household to move (i.e., relocate) in the next two years. The attack assumes that the classifier has been made publically available and that the attacker has access to information about a certain number of target individuals. That attacker...
conference paper 2022
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Garofalo, Giuseppe (author), Slokom, M. (author), Preuveneers, Davy (author), Joosen, Wouter (author), Larson, M.A. (author)
We explore how data modification can enhance privacy by examining the connection between data modification and machine learning. Specifically, machine learning “meets” data modification in two ways. First, data modification can protect the data that is used to train machine learning models focusing it on the intended use and inhibiting...
book chapter 2022
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Ekstrand, Michael D. (author), Chaney, Allison (author), Castells, Pablo (author), Burke, Robin (author), Rohde, David (author), Slokom, M. (author)
There is significant interest lately in using synthetic data and simulation infrastructures for various types of recommender systems research. However, there are not currently any clear best practices around how best to apply these methods. We proposed a workshop to bring together researchers and practitioners interested in simulating...
conference paper 2021
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Slokom, M. (author), Hanjalic, A. (author), Larson, M.A. (author)
In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective,...
journal article 2021
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Strucks, Christopher (author), Slokom, M. (author), Larson, M.A. (author)
Past research has demonstrated that removing implicit gender information from the user-item matrix does not result in substantial performance losses. Such results point towards promising solutions for protecting users’ privacy without compromising prediction performance, which are of particular interest in multistakeholder environments. Here,...
conference paper 2019
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Slokom, M. (author), Larson, M.A. (author), Hanjalic, A. (author)
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...
conference paper 2019
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Larson, M.A. (author), Slokom, M. (author)
Hypotargeting for recommender systems (hyporec) is the idea of controlling the number of unique lists of items that a recommender system can recommend to users during a given time period. The main advantage of hyporec is oversight. If a recommender system offers only a finite number of unique lists, then it becomes feasible for a person...
conference paper 2019
Searched for: author%3A%22Slokom%2C+M.%22
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