A survey on data provenance in IoT

Abstract

Internet of Things (IoT), as a typical representation of cyberization, enables the interconnection of physical things and the Internet, which provides intelligent and advanced services for industrial production and human lives. However, it also brings new challenges to IoT applications due to heterogeneity, complexity and dynamic nature of IoT. Especially, it is difficult to determine the sources of specified data, which is vulnerable to inserted attacks raised by different parties during data transmission and processing. In order to solve these issues, data provenance is introduced, which records data origins and the history of data generation and processing, thus possible to track the sources and reasons of any problems. Though some related researches have been proposed, the literature still lacks a comprehensive survey on data provenance in IoT. In this paper, we first propose a number of design requirements of data provenance in IoT by analyzing the features of IoT data and applications. Then, we provide a deepinsight review on existing schemes of IoT data provenance and employ the requirements to discuss their pros and cons. Finally, we summarize a number of open issues to direct future research.

An Extended Framework of Privacy-Preserving Computation With Flexible Access Control

“…C LOUD computing can efficiently store and process data in the Internet by taking advantage of its huge volume of resources and great computation. The arrival of cyberization era has led to the demand of massive data generation, analysis and processing, which causes high computation overhead that cannot be handled by local devices [2], [3], [4]. Outsourcing computing to the cloud can greatly benefit resourceconstrained users [5].…”

Investigating the Adoption of Hybrid Encrypted Cloud Data Deduplication With Game Theory

“…Data provenance prevents attacks performed during data transmission and processing by discovering data sources. Hu et al [36] classified current data provenance solutions into three categories based on what technologies are applied: logs, cryptographic algorithms, and blockchain. For example, Suen et al [85] proposed a data-centric logging mechanism to record data events at both file and block level.…”

“…Storing data in the cloud saves local storage space, reduces data management costs, and enables data users to easily access their data in the cloud everywhere and at any time. In recent years, various ap-Introduction proaches have been proposed for securely and efficiently outsourcing personal data to the cloud, including protecting data security and privacy [15,17,36,60,88,90,92,93,100], controlling data access [19,20,21,24,76,91,103,104,105,107,111,121], cloud data processing [22,106,118], verifiable computation [109,115,116], and encrypted data deduplication [23,34,50,51,55,56,61,99,101,102,108,110,117].…”

Orchestrating and sharing large multimodal data for transparent and reproducible research

“…The high-dimensionality, complexity, and scale of pharmacogenomic data present unprecedented challenges for researchers in the biomedical field, in regards to their ability to effectively manage, track, and process the data. The nature of heterogeneous and complex data negatively impacts data provenance, through incomplete or no accompaniment of metadata for a dataset, resulting in the uncertainty of a data lineage [22][23][24] . Because the granularity of metadata is a determinant of the value of a dataset 25 , it should provide a rich description of dataset content, following the FAIR data principles, which includes information about dataset origin, how it was generated, if there were any modifications that were made to it from precedent versions, and what these modifications were 14,26,27 .…”