System for Analyzing Crime News by Mining Live Data Streams with Preserving Data Privacy

Patil, Rahul; Patil, Pramod; Kanase, Sayali; Bhegade, Nikita; Chavan, Vaishnavi; Kashetwar, Shreyas

doi:10.1007/978-981-16-5157-1_63

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…These approaches employ variety of operations such as generalization [14], suppression [15], bucketization [16], hash functions [17], cryptographic primitives [18], lattice-based encryption [19], parameter sharing [20], masking [21,22], pseudonyms [23][24][25][26], and joint operations [27][28][29][30] in order to preserve the privacy of the individual. Recently, machine learning (ML) approaches have also been employed to preserve the privacy of individuals in data analysis and publishing [31][32][33][34][35].…”

Section: State-of-the-art Privacy Preserving Approachesmentioning

confidence: 99%

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

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Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications is has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings.

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Section: State-of-the-art Privacy Preserving Approachesmentioning

confidence: 99%

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

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“…Stream data have many potential benefits in time-sensitive and IoT-based applications. Privacy preservation in stream data has been extensively studied in recent years [44]- [46]. Stream data are usually collected in tuples; hence, privacy preservation is more challenging, compared to other types of data [47].…”

Section: H Cams For Stream Datamentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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Fairness optimisation with multi-objective swarms for explainable classifiers on data streams

Pham,

Tran,

Nguyen

et al. 2024

Complex Intell. Syst.

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Recently, advanced AI systems equipped with sophisticated learning algorithms have emerged, enabling the processing of extensive streaming data for online decision-making in diverse domains. However, the widespread deployment of these systems has prompted concerns regarding potential ethical issues, particularly the risk of discrimination that can adversely impact certain community groups. This issue has been proven to be challenging to address in the context of streaming data, where data distribution can change over time, including changes in the level of discrimination within the data. In addition, transparent models like decision trees are favoured in such applications because they illustrate the decision-making process. However, it is essential to keep the models compact because the explainability of large models can diminish. Existing methods usually mitigate discrimination at the cost of accuracy. Accuracy and discrimination, therefore, can be considered conflicting objectives. Current methods are still limited in controlling the trade-off between these conflicting objectives. This paper proposes a method that can incrementally learn classification models from streaming data and automatically adjust the learnt models to balance multi-objectives simultaneously. The novelty of this research is to propose a multi-objective algorithm to maximise accuracy, minimise discrimination and model size simultaneously based on swarm intelligence. Experimental results using six real-world datasets show that the proposed algorithm can evolve fairer and simpler classifiers while maintaining competitive accuracy compared to existing state-of-the-art methods tailored for streaming data.

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System for Analyzing Crime News by Mining Live Data Streams with Preserving Data Privacy

Cited by 4 publications

References 10 publications

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Fairness optimisation with multi-objective swarms for explainable classifiers on data streams

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