Secure data provenance compression using arithmetic coding in wireless sensor networks

Hussain, Syed Rafiul; Wang, Changda; Sultana, Salmin; Bertino, Elisa

doi:10.1109/pccc.2014.7017068

Cited by 31 publications

(25 citation statements)

References 14 publications

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“…In IoT the data transmissed and consumed in very large amount due to increaing number of participants hence huge amount of data will tansmited to achieve efficent storage and processing is difficult due to space complexicity and energy computation.The concept of meta data is used to determne information provenance so tracing data object in big data is become very difficult, [14] conssumption of network banwidth in another issue faced to avoid performance of system 3) Indexing provenance The IoT system likely to large it is very difficult to look each and every data and find name, to avoid this searching datasets of attributes can done to found in meta data depending up on the user the query will change depending u on the goal, another way is indexing can be done in few cases Data Citation can be done but it required efficient lookups in all dimensions to retrieve data.…”

Section: ) Data Processing and Storagementioning

confidence: 99%

“…Lineage gives a notion of the quality of GIS datasets based on the source data GIS applications use a cartographic model to transform and derive spatial layers LIP [15] uses a data structure called frame which describes the metadata of a spatial layer. Three types of frames are available: source frame, containing quality information about the source layers, such as scale and projection; command frame, with the commands used to derive intermediate and product layers; and product frames that has metadata specific to the product layers…”

Section: ) Lineage Information Programmentioning

confidence: 99%

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Security & privacy in IoT Data Provenance

T.K¹,

Jebakumar²

2018

IJET

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Abstract-IoT has become most emerging trend in engineering and technology, that has bring relevant change in industries. IoT has many applications like healthcare, agriculture etc. Since IoT deals with huge number of objects that need to generate and consume different types of data and entity. The need of security and privacy challenges should enforced. The data provenance deals with detecting creation and propagation of data, The main purpose of the paper is to address security and privacy issues by implementing data provenance in IoT. To this end we will mention different challenges to meet security and privacy in IoT data provenance

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Section: ) Data Processing and Storagementioning

confidence: 99%

Section: ) Lineage Information Programmentioning

confidence: 99%

Security & privacy in IoT Data Provenance

T.K¹,

Jebakumar²

2018

IJET

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“…Hussain et al [11] proposed an arithmetic coding-based provenance (ACP) scheme. Unlike most of the provenance schemes, its provenance size for a packet is not directly proportional to the number of packet transmission hops, but to the occurrence probability of the packet path in the WSN.…”

Section: Acpmentioning

confidence: 99%

“…To address such issues, a distributed message digest scheme, the AM-FM sketch scheme [7] with adjustable output length relating to the false positive rate has been adopted in recent provenance schemes [11,22,23,27]. The AM-FM sketch scheme prevents the binding data's size from growing beyond the range ½ð1 À Þk; ð1 þ Þ2 with probability 1 À d, where k is the sample size of the provenance; 0\d\1 and \1 are the false positive and false negative rates related to k assuming that Oðk !…”

Section: Provenance Bindingmentioning

confidence: 99%

Provenance for Wireless Sensor Networks: A Survey

2016

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In wireless sensor networks (WSNs), provenance records the data source, forwarding, and aggregating information of data packets on their way to the base station. Provenance is critical for assessing the trustworthiness of the received data, diagnosing network failures, detecting early signs of attacks, etc. However, because the provenance size expands rapidly with the increase in packet transmission hops, the provenance schemes developed for use in wired computer networks are not generally applicable to WSNs. Therefore, specific provenance techniques have been developed for WSNs that take into account the constrained resources of sensor nodes. In this paper, we survey such techniques. Special focus in the paper is devoted to a systematic and comprehensive classification of the solutions proposed in the literature. We review each solution by highlighting its pros and cons. Finally, we discuss recent trends in provenance encoding schemes for WSNs.

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“…Because of the diversity of the environment and the large number of sensor types involved, in order to use the reliable information to make an accurate decision, it is essential to evaluate the trustworthiness of the received data at the base station (BS) of a WSN. In practice, there are some examples of significant losses because the faulty data are used [1].…”

Section: Introductionmentioning

confidence: 99%

Cluster‐Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

Akhtar

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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In wireless sensor networks (WSNs), data provenance records the data source and the forwarding and the aggregating information of a packet on its way to the base station (BS). To conserve the energy and wireless communication bandwidth, the provenances are compressed at each node along the packet path. To perform the provenances compression in resource-tightened WSNs, we present a cluster-based arithmetic coding method which not only has a higher compression rate but also can encode and decode the provenance in an incremental manner; i.e., the provenance can be zoomed in and out like Google Maps. Such a decoding method raises the efficiencies of the provenance decoding and the data trust assessment. Furthermore, the relationship between the clustering size and the provenance size is formally analyzed, and then the optimal clustering size is derived as a mathematical function of the WSN’s size. Both the simulation and the test-bed experimental results show that our scheme outperforms the known arithmetic coding based provenance compression schemes with respect to the average provenance size, the energy consumption, and the communication bandwidth consumption.

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Secure data provenance compression using arithmetic coding in wireless sensor networks

Cited by 31 publications

References 14 publications

Security & privacy in IoT Data Provenance

Security & privacy in IoT Data Provenance

Provenance for Wireless Sensor Networks: A Survey

Cluster‐Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

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