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Mr. Antreas Pogiatzis
University of Greenwich

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0 Natural Language Processing
0 Cybersecurity
0 Privacy & Security
0 Distributed Ledger Technologies
0 artificial intelligence

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Journal article
Published: 28 December 2020 in Applied Sciences
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This work presents an event-driven Extract, Transform, and Load (ETL) pipeline serverless architecture and provides an evaluation of its performance over a range of dataflow tasks of varying frequency, velocity, and payload size. We design an experiment while using generated tabular data throughout varying data volumes, event frequencies, and processing power in order to measure: (i) the consistency of pipeline executions; (ii) reliability on data delivery; (iii) maximum payload size per pipeline; and, (iv) economic scalability (cost of chargeable tasks). We run 92 parameterised experiments on a simple AWS architecture, thus avoiding any AWS-enhanced platform features, in order to allow for unbiased assessment of our model’s performance. Our results indicate that our reference architecture can achieve time-consistent data processing of event payloads of more than 100 MB, with a throughput of 750 KB/s across four event frequencies. It is also observed that, although the utilisation of an SQS queue for data transfer enables easy concurrency control and data slicing, it becomes a bottleneck on large sized event payloads. Finally, we develop and discuss a candidate pricing model for our reference architecture usage.

ACS Style

Antreas Pogiatzis; Georgios Samakovitis. An Event-Driven Serverless ETL Pipeline on AWS. Applied Sciences 2020, 11, 191 .

AMA Style

Antreas Pogiatzis, Georgios Samakovitis. An Event-Driven Serverless ETL Pipeline on AWS. Applied Sciences. 2020; 11 (1):191.

Chicago/Turabian Style

Antreas Pogiatzis; Georgios Samakovitis. 2020. "An Event-Driven Serverless ETL Pipeline on AWS." Applied Sciences 11, no. 1: 191.

Journal article
Published: 14 December 2020 in Applied Sciences
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Information privacy is a critical design feature for any exchange system, with privacy-preserving applications requiring, most of the time, the identification and labelling of sensitive information. However, privacy and the concept of “sensitive information” are extremely elusive terms, as they are heavily dependent upon the context they are conveyed in. To accommodate such specificity, we first introduce a taxonomy of four context classes to categorise relationships of terms with their textual surroundings by meaning, interaction, precedence, and preference. We then propose a predictive context-aware model based on a Bidirectional Long Short Term Memory network with Conditional Random Fields (BiLSTM+CRF) to identify and label sensitive information in conversational data (multi-class sensitivity labelling). We train our model on a synthetic annotated dataset of real-world conversational data categorised in 13 sensitivity classes that we derive from the P3P standard. We parameterise and run a series of experiments featuring word and character embeddings and introduce a set of auxiliary features to improve model performance. Our results demonstrate that the BiLSTM+CRF model architecture with BERT embeddings and WordShape features is the most effective (F1 score 96.73%). Evaluation of the model is conducted under both temporal and semantic contexts, achieving a 76.33% F1 score on unseen data and outperforms Google’s Data Loss Prevention (DLP) system on sensitivity labelling tasks.

ACS Style

Antreas Pogiatzis; Georgios Samakovitis. Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data. Applied Sciences 2020, 10, 8924 .

AMA Style

Antreas Pogiatzis, Georgios Samakovitis. Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data. Applied Sciences. 2020; 10 (24):8924.

Chicago/Turabian Style

Antreas Pogiatzis; Georgios Samakovitis. 2020. "Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data." Applied Sciences 10, no. 24: 8924.