Presentation
Extended Abstract of Privacy-enhanced AI Assistants based on Dialogues and Case Similarity [Póster]
Author/s | Zhan, Xiao
Sarkadi, Stefan Such, José |
Editor | Varela Vaca, Ángel Jesús
Ceballos Guerrero, Rafael Reina Quintero, Antonia María |
Publication Date | 2024 |
Deposit Date | 2024-08-26 |
Published in |
|
ISBN/ISSN | 978-84-09-62140-8 |
Abstract | Personal assistants (PAs) such as Amazon Alexa, Google Assistant and Apple Siri are now widespread. However, without adequate safeguards and controls their use may lead to privacy risks and violations. We propose a model ... Personal assistants (PAs) such as Amazon Alexa, Google Assistant and Apple Siri are now widespread. However, without adequate safeguards and controls their use may lead to privacy risks and violations. We propose a model for privacy enhancing PAs. The model is an interpretable AI architecture that combines 1) a dialogue mechanism for understanding the user and getting online feedback from them, with 2) a decision making mechanism based on case-based reasoning considering both user and scenario similarity. We evaluate our model using real data about users’ privacy preferences, and compare its accuracy and demand for user involvement with both online machine learning and other, more interpretable, AI approaches. Our results show that our proposed architecture is more accurate and requires less intervention from the users than existing approaches. |
Citation | Zhan, X., Sarkadi, S. y Such, J. (2024). Extended Abstract of Privacy-enhanced AI Assistants based on Dialogues and Case Similarity. En Jornadas Nacionales de Investigación en Ciberseguridad (JNIC) (9ª.2024. Sevilla) (482-483), Sevilla: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática. |
Files | Size | Format | View | Description |
---|---|---|---|---|
JNIC24_500.pdf | 946.0Kb | [PDF] | View/ | |