A journey on privacy protection strategies in big data

Viji, D.; Saravanan, KA; Hemavathi, D.

doi:10.1109/iccons.2017.8250688

Cited by 6 publications

(11 citation statements)

References 10 publications

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“…We also observed that the proposed phases of the data lifecycles also vary [56] while often for the same phase, different terms are used. For example, in the case of data collection, some studies use the term "generation" [95,96] while in other data lifecycles call it "receive" [17,90], "acquir" [55,63,73,97], or data "capture" [84]. So, there is a lot of confusion for the research community and practitioners to understand and use such phases and associate them with management practices in their organizations.…”

Section: Research Gapsmentioning

confidence: 99%

“…We assign a "mandatory" category to phase that has > 70% of appearance in the data lifecycles, and in other cases, we assigned it "optional" category as shown in Table 2. Additionally, we provide detailed information about the data lifecycles and their [15,53,58,134,143], receive [17], acquisition [13,117], retrieve [115,121], produce [140], acquire [55,63,90,97,147], capture [84,94,126,130,135,148], generation [26,56,95,96,[118][119][120], create [47, 109-111, 116, 124, 149] Collection P3 Integration [17,26,109,124,126,129,130,146,148,153,155], integrate [15,28,116,140,156], aggregate…”

Section: Approach To Dalifmentioning

confidence: 99%

“…The base registries support PAs to eliminate data silos and maximize the data's re-use across the public sector entities easily and inexpensively [15,58]. In the storage phase, different modern tools and technologies are required to store big data like NoSQL, NewSQL, Big Data Query Platforms, Hadoop Distributed File System (HDFS), and cloud storage technologies [50,95,96,168,169]. Moreover, several NoSQL technologies, like HBase, MongoDB, Cassandra, CouchDB, DynamoDB, Riak, Redis, Neo4J.…”

Section: Analysis Phase Key Functionsmentioning

confidence: 99%

“…• Perform continuous work on data storage with improved data structures and backing fault tolerance [65,85]. • Adopting approaches based on encryption techniques ensures privacy protection in the data storage phase [95,96]. • Implement a document and content management system that offer electronic documents and electronic images of paper documents storage, versioning, security, metadata management, content indexing, and retrieval capabilities [70,71].…”

Section: Storage Phase Key Functionsmentioning

confidence: 99%

“…• Data resources, data infrastructure, and data management should be forecast to deliver continuity and archive data for as long as required [66]. • Use of appropriate anonymization techniques like generalization and suppression to protect data privacy during this phase [95,96].…”

Section: Storage Phase Key Functionsmentioning

confidence: 99%

See 4 more Smart Citations

DaLiF: a data lifecycle framework for data-driven governments

2021

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The public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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Section: Research Gapsmentioning

confidence: 99%

Section: Approach To Dalifmentioning

confidence: 99%

Section: Analysis Phase Key Functionsmentioning

confidence: 99%

Section: Storage Phase Key Functionsmentioning

confidence: 99%

Section: Storage Phase Key Functionsmentioning

confidence: 99%

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DaLiF: a data lifecycle framework for data-driven governments

2021

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Applying Machine Learning for Securing Data Storage Using Random DNA Sequences and Pseudo-Random Sequence Generators

Afify

Rahouma

2021

Advances in Intelligent Systems and Computing

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Secure Drone Communications using MQTT protocol

Sushma Polasi,

Hara Gopal Venkata Vajjha

2024

IJCESEN

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With the revolutionary change in emerging technologies, the usage of drones or unmanned aerial vehicles (UAV) has exponentially increased in different sectors like Industry, healthcare, military, agriculture, real estate, manufacturing, logistics, energy and many more utilities. This rapid growth creates a concern for the secure communication between the internal communication modules and the ground based computer system used for controlling the UAV. Intruder can hack into the device and attack the internal communication device with the injection of the malicious code which can lead to the malfunction of the aircraft. Security issues related to the communication between the internal modules of the drone and the ground based computer system is of major concern and is crucial. UAVs have to operate in constrained networks with limited bandwidth. In such a constrained environment MQTT protocol can be an excellent protocol. No cryptographic techniques are used to retain the simplicity and the light weight of the MQTT protocol. This contributes for the large scope for emerging with new solutions in the protection issues of MQTT communications. This paper mainly focuses on Armstrong number encryption standard algorithm for providing a computationally simple yet a secure and strong algorithm for UAVs encryption and decryption process on the communication links using MQTT protocol.

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A journey on privacy protection strategies in big data

Cited by 6 publications

References 10 publications

DaLiF: a data lifecycle framework for data-driven governments

DaLiF: a data lifecycle framework for data-driven governments

Applying Machine Learning for Securing Data Storage Using Random DNA Sequences and Pseudo-Random Sequence Generators

Secure Drone Communications using MQTT protocol

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