Main Reference PaperContext-driven Granular Disclosure Control for Internet of Things Applications, IEEE Transactions on Big Data, 2018 [Java/Hadoop]
  • This work proposes a framework for privacy preservation of IoT data. In this work, the spatiotemporal data stream is protected by using digital watermarking and data scrambling. To achieve spatiotemporal privacy, a two-tier privacy preservation is proposed, one for privacy preservation of spatial data at the data collection stage, and the other for temporal data that is applied at the data storage stage.

Description
  • This work proposes a framework for privacy preservation of IoT data. In this work, the spatiotemporal data stream is protected by using digital watermarking and data scrambling. To achieve spatiotemporal privacy, a two-tier privacy preservation is proposed, one for privacy preservation of spatial data at the data collection stage, and the other for temporal data that is applied at the data storage stage.

  • To achieve the privacy of IoT data.

Aim & Objectives
  • To achieve the privacy of IoT data.

  • A technique is contributed to improve the scalability of IoT data.

Contribution
  • A technique is contributed to improve the scalability of IoT data.

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Order To Delivery
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