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Aggregatably Verifiable Data Streaming
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PRE
AI
DOI：10.1109/JIOT.2024.3388448.png)
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Abstract
En 中文
In various real-time applications like intelligent transportation and stock trading systems, clients continuously generate the so-called data streaming that is sensitive to both the position and content. Due to the limitations of local storage resources, clients usually have to outsource the generated data to cloud servers that are not fully trusted. The primitive of verifiable data streaming (VDS) protocol was introduced to guarantee the integrity of the outsourced data streaming. Although many VDS protocols have been proposed to improve the efficiency and security of the original one, they mainly focus on how to verifiably retrieve specific data items, without considering the requirement of retrieving aggregated results. However, such a requirement is desirable in many practical applications that only need the aggregated results of the outsourced streaming data, such as satellite cloud atlas and real-time traffic data. In this article, we introduce a new primitive named aggregatably VDS (AVDS) that allows a data user to retrieve aggregated results of designated data items, while guaranteeing the validity of the aggregated results. Specifically, we introduce a new authenticated data structure named chameleon linear-map vector commitment (CLVC) and also provide a concrete construction. Furthermore, we propose a general framework of AVDS protocols from the building block of CLVC. The proposed AVDS protocol is proven to be secure in the standard model. Theoretical analysis and experimental results indicate that the proposed AVDS protocol extends previous VDS protocols in terms of functionality while having comparable computation and communication overhead.
Keywords:
Aggregation query
chameleon linear-map vector commitment (CLVC)
outsourced storage
verifiable data streaming (VDS)
Journal
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8.9
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1.4W
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7.8W