Advances in Data Management in the Big Data Era

Azzini, Antonia; Barbon, Sylvio; Bellandi, Valerio; Catarci, Tiziana; Ceravolo, Paolo; Cudré-Mauroux, Philippe; Maghool, Samira; Pokorný, Jaroslav; Scannapieco, Monica; Sèdes, Florence; Tavares, Gabriel Marques; Wrembel, Robert

doi:10.1007/978-3-030-81701-5_4

Cited by 8 publications

(2 citation statements)

References 94 publications

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“…Graph Embedding (GE) in general terms is a data preparation procedure mapping the existing information of a network as much as possible in a unified usable data format to input ML algorithms for implementing several downstream tasks. Many algorithms such as Matrix factorization-based [9,27], Random walk-based [15,29,30], and Neural network-based algorithms [16,19] are developed in order to retrieve as much as possible the properties of the network and map them in a latent space [3]. Most of these algorithms have been however criticized for adding a level of complexity to ML results [5].…”

Section: Graph Embeddingmentioning

confidence: 99%

Validating Vector-Label Propagation for Graph Embedding

Bellandi

Damiani²,

Ghirimoldi³

et al. 2022

Lecture Notes in Computer Science

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Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.

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Section: Graph Embeddingmentioning

confidence: 99%

Validating Vector-Label Propagation for Graph Embedding

Bellandi

Damiani²,

Ghirimoldi³

et al. 2022

Lecture Notes in Computer Science

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“…This surge in data collection encompasses various aspects of our lives, including social media platforms and e-commerce websites. Consequently, businesses and governments have begun harnessing this vast amount of data for analytical purposes, enabling them to gain valuable insights and make informed decisions [4]. However, the rapid growth in data generation has also raised concerns regarding data privacy and security.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Anonymization of Sensitive Personal Data Using a Service-Based Architecture

Giampaolo,

Izzo,

Siccardi

et al. 2023

2023 IEEE International Conference on Web Services (ICWS)

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Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.

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