PRIMA: An End-to-End Framework for Privacy at Scale

Antonatos, Spiros; Braghin, Stefano; Holohan, Naoise; Gkoufas, Yiannis; Aonghusa, Pól Mac

doi:10.1109/icde.2018.00171

Cited by 15 publications

(17 citation statements)

References 17 publications

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“…However, their approach does not address the potential memory exhaustion unable to accommodate increasing number of intermediate data produced as the number of iteration increases. PRIMA [16] proposes an anonymization strategy for Mondrian algorithm with Optimal Lattice Anonymization (OLA) which is used to define the utility and generalization level rules in order to limit the data utility loss. Reference [22] proposes a distributed Mondrian approach by splitting the input data to the partitions allocated to each node of cluster by using Spark k-mean.…”

Section: Related Workmentioning

confidence: 99%

“…Spark has extended its scalability aspect in addition to offering a new set of advanced features more suited for the algorithms dealing with many different types of big data operations [14]. With the surge in the population of Spark and shift from MapReduce approach, many Spark-based data anonymization techniques have been proposed [15][16][17][18][19]. However, these existing proposals often tend to focus their efforts on improving and readdressing the scalability aspects to be more suited for Spark instead of investigating the suitability of Spark as a platform of choice for data anonymization techniques.…”

Section: Introductionmentioning

confidence: 99%

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In-Memory Data Anonymization Using Scalable and High Performance RDD Design

Bazai

Jang-Jaccard

2020

Electronics

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Recent studies in data anonymization techniques have primarily focused on MapReduce. However, these existing MapReduce based approaches often suffer from many performance overheads due to their inappropriate use of data allocation, expensive disk I/O access and network transfer, and no support for iterative tasks. We propose “SparkDA” which is a new novel anonymization technique that is designed to take the full advantage of Spark platform to generate privacy-preserving anonymized dataset in the most efficient way possible. Our proposal offers a better partition control, in-memory operation and cache management for iterative operations that are heavily utilised for data anonymization processing. Our proposal is based on Spark’s Resilient Distributed Dataset (RDD) with two critical operations of RDD, such as FlatMapRDD and ReduceByKeyRDD, respectively. The experimental results demonstrate that our proposal outperforms the existing approaches in terms of performance and scalability while maintaining high data privacy and utility levels. This illustrates that our proposal is capable to be used in a wider big data applications that demands privacy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-Memory Data Anonymization Using Scalable and High Performance RDD Design

Bazai

Jang-Jaccard

2020

Electronics

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“…Ethical data sharing requires transforming data via aggregation or other means to ensure that it is no longer identifiable at a level that jeopardizes privacy and cannot by “deanonymized” when combined with other data sets (Helveston, ; Wu, ). All anonymization techniques will inherently cause a loss in the information content and utility of the data (Antonatos et al, ). To minimize the effect of this loss and meet FAIR standards, it is critical to also include detailed information about the anonymization procedure via metadata and sharing code, ideally using open‐source tools integrating version control for transparency, to allow for interoperability and usability by other researchers (Bakker, ; Lowndes et al, ; Stagge et al, ).…”

Section: Addressing Privacy Concerns With Open and Ethical Data Managmentioning

confidence: 99%

Balancing Open Science and Data Privacy in the Water Sciences

Zipper

Whitney

Deines

et al. 2019

Water Resources Research

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Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio‐hydrology), there is the potential for well‐meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here we identify the contexts in which privacy violations are most likely to occur, such as working with high‐resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio‐environmental research and that progress toward these goals be rooted in open and ethical data management.

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“…Ethical data sharing requires transforming data via aggregation or other means to ensure that it is no longer identifiable at a level that jeopardizes privacy and cannot by 'de-anonymized' when combined with other datasets (Helveston, 2015;Wu, 2013). All anonymization techniques will inherently cause a loss in the information content and utility of the data (Antonatos et al, 2018).…”

Section: Sharing Private Datamentioning

confidence: 99%

Balancing Open Science and Data Privacy in the Water Sciences

Zipper¹,

Whitney²,

Deines³

et al. 2019

Preprint

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Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio-hydrology), there is the potential for well-meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here, we identify the contexts in which privacy violations are most likely to occur, such as working with high-resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio-environmental research and that progress toward these goals be rooted in open and ethical data management.

show abstract

PRIMA: An End-to-End Framework for Privacy at Scale

Cited by 15 publications

References 17 publications

In-Memory Data Anonymization Using Scalable and High Performance RDD Design

In-Memory Data Anonymization Using Scalable and High Performance RDD Design

Balancing Open Science and Data Privacy in the Water Sciences

Balancing Open Science and Data Privacy in the Water Sciences

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