Privacy-preserving Federated Singular Value Decomposition

Modern Singular Value Decomposition (SVD) computation dates back to the 1960s when the basis for the eigensystem package and linear algebra package routines was created. Since then, SVD has gained attraction and been widely applied in various scenarios, such as recommendation systems and principal component analyses. Federated SVD has recently emerged, where different parties could collaboratively compute SVD without exchanging raw data. Besides its inherited privacy protection, noise injection could be utilized to further increase the privacy guarantee of this privacy-friendly technique. This paper advances the state-of-science by improving an existing Federated SVD scheme with two-fold contributions. First, we revise its privacy guarantee in terms of Differential Privacy, the de-facto data privacy standard of the 21st century. Second, we increase its utility by reducing the added noise, which is achieved by employing Secure Aggregation, a cryptographic technique to prevent information leakage. Using a recommendation system use-case with real-world data, we demonstrate that our scheme outperforms the state-of-the-art Federated SVD solution

A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy

PRIVACY-PRESERVING SVD-BASED COLLABORATIVE FILTERING ON PARTITIONED DATA

Collaborative filtering (CF) systems are widely employed by many e-commerce sites for providing recommendations to their customers. To recruit new customers, retain the current ones, and gain competitive edge over competing companies, online vendors need to offer accurate predictions efficiently. Therefore, providing precise recommendations efficiently to many users in real time is imperative. Singular value decomposition (SVD) is applied to CF to achieve such goal. SVD-based CF systems offer reliable and accurate predictions when they own large enough data. Data collected for CF purposes, however, might be split between different companies, even competing ones. Some vendors, especially newly established ones, might have problems with available data. To increase mutual advantages, provide richer CF services, and overcome problems caused by inadequate data, companies want to integrate their data. However, due to privacy, legal, and financial reasons, they do not want to combine their data. In this article, we investigate how to provide SVD-based referrals on partitioned (horizontally or vertically) data without greatly jeopardizing data holders' privacy. We conduct real data-based experiments to assess our schemes' overall performance and analyze them in terms of privacy and supplementary costs. Our results show that it is possible to provide accurate SVD-based referrals on integrated data while preserving e-companies' privacy.Privacy, partitioned data, e-commerce, CF, SVD, prediction

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities