Poisson process with fuzzy rates

Li, Shunqin

doi:10.1007/s10700-010-9082-1

Cited by 4 publications

(3 citation statements)

References 17 publications

(6 reference statements)

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“…They predict the data arrival rate to resize the buffer to minimize the information loss. For the calculation of data arrival rate they utilize Poisson probability model [41] by assuming the data streams as a sequence of events occurred in a fixed time interval.…”

Section: Related Work a Data Stream Anonymizationmentioning

confidence: 99%

$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams

Otgonbayar

Pervez

Dahal

2020

IEEE Internet Things J.

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The Internet-of-Things (IoT) has formed a whole new layer of the world built on internet, reaching every connected devices, actuators and sensors. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provides varied data streams due to the nature of the individual's preference and versatile devices pool. The conventional single tuple expiration driven sliding window method is not adequate to provide efficient anonymization. Furthermore, minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real datasets shows that X-BAND is effective and efficient compared to famous conventional anonymization algorithm FADS. X-BAND demonstrated 5% to 11% and 1% to 3% less information loss on real dataset Adult and PM2.5 respectively while performing similar on clustering, comparable to re-using suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.

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Section: Related Work a Data Stream Anonymizationmentioning

confidence: 99%

$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams

Otgonbayar

Pervez

Dahal

2020

IEEE Internet Things J.

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“…3: Screenshot of our Crime Reporting Application of success that occurs within a given unit of measure. This property of the Poisson distribution makes viewing the arrival rate of the reported crime data as a series of events occurring within a fixed time interval at an average rate that is independent of occurrence of the time of the last event [6]. Only one parameter needs to be known, the rate at which the events occur which in our case is the rate at which crime reporting occurs.…”

Section: Anonymization Layer: Data Stream Anonymizationmentioning

confidence: 99%

On Anonymizing Streaming Crime Data: A Solution Approach for Resource Constrained Environments

Sakpere

Kayem

2018

IFIP Advances in Information and Communication Technology

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…They predict the data arrival rate to resize the buffer to minimize the information loss. For the calculation of data arrival rate they utilized Poisson probability model [37] by assuming the data streams as a sequence of events occurred in a fixed time interval.…”

Section: Introductionmentioning

confidence: 99%