Aggregatably Verifiable Data Streaming

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.

I. Introduction

The emergence and popularity of several new computing paradigms like cloud computing and mobile-edge computing have enabled the widespread deployment of various real-time applications, e.g., intelligent transportation, weather forecasting, and stock trading systems. In these scenarios, clients equipped with sensors like smart cars and meteorological satellites continuously collect real-time data (i.e., the so-called data streaming) from the environment. Due to the limited resources of both storage and computation, clients have to outsource the generated data to cloud servers. However, those servers are not always fully trusted [1], [2]. In other words, driven by its financial interests, a malicious server may delete or tamper with the outsourced data. As a result, those downstream data analysis tasks based on that data are misguided and produce biased results. Therefore, it is critical to guarantee the integrity of the outsourced data streaming.