Preserving data privacy in machine learning systems

El Mestari, Soumia Zohra; Lenzini, Gabriele; Demirci, Huseyin

doi:10.1016/j.cose.2023.103605

Cited by 22 publications

(2 citation statements)

References 37 publications

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“…According to Boehm et al [11], 57% of customers have lost trust in organizations and 31% of customers no longer use the services of organizations that have suffered data breaches. Such privacy risks can be managed by using techniques such as fully homomorphic encryption, adversarial learning, and secure components, from data pre-processing to validation of results, all of which are included, and PET covering all phases of the model pipeline [12].…”

Section: Privacy Issuementioning

confidence: 99%

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Baru Khan Bau

2024

IJITCSA

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Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

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Section: Privacy Issuementioning

confidence: 99%

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Baru Khan Bau

2024

IJITCSA

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“…It should be mentioned that it is crucial to consider the security and privacy preservation in ML models. Reviews and surveys on the current state of knowledge regarding privacy preservation in ML workflows can be found, for example, in [21][22][23][24]. In privacy-preserving machine learning, the primary objective is to ensure the continuous encryption or maintenance of raw data in ciphertext form during both training and prediction processes.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Simulation of Mallard Movements

Einarson,

Frisk,

Klonowska

et al. 2024

Applied Sciences

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Machine learning (ML) is increasingly used in diverse fields, including animal behavior research. However, its application to ambiguous data requires careful consideration to avoid uncritical interpretations. This paper extends prior research on ringed mallards where sensors revealed their movements in southern Sweden, particularly in areas with small lakes. The primary focus is to distinguish the movement patterns of wild and farmed mallards. While well-known statistical methods can capture such differences, ML also provides opportunities to simulate behaviors outside of the core study span. Building on this, this study applies ML techniques to simulate these movements, using the previously collected data. It is crucial to note that unrefined application of ML can lead to incomplete or misleading outcomes. Challenges in the data include disparities in swimming and flying records, farmed mallards’ biased data due to feeding points, and extended intervals between data points. This research highlights these data challenges, while identifying discernible patterns, as well as proposing approaches to meet such challenges. The key contribution lies in separating incompatible data and, through different ML models, handle these separately to enhance the reliability of the simulation models. This approach ensures a more credible and nuanced understanding of mallard movements, demonstrating the importance of critical analysis in ML applications in wildlife studies.

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