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In many applications such as sensor networks, ehealthcare and environmental monitoring, data is continuously streamed and combined from multiple resources in order to make decisions based on the aggregated data streams. One major concern in these applications is assuring high trustworthiness of the aggregated data stream for correct decision-making. For example, an adversary may compromise a few data-sources and introduce false data into the aggregated data-stream and cause catastrophic consequences. In this work, we propose a novel method for verifying data integrity by embedding several signature codes within data streams known as digital watermarking. Therefore, the integrity of the data streams can be verified by decoding the embedded signatures even as the data go through multiple stages of aggregation process. Although the idea of secure data aggregation based on digital watermarking has been explored before, we aim to improve the efficiency of the scheme by examining several signature codes that could also decrease the watermark detection complexity. This is achieved by simultaneous embedding of several shifted watermark patterns into aggregated data stream, such that the contribution of each data-source is hidden in the relative shifts of the patterns. We, also, derive conditions to preserve the main statistical properties of data-streams prior to the embedding procedure. Therefore, we can guarantee that the embedding procedure does not compromise the usability of data streams for any operations that depends on these statistical characteristics. The simulation results show that the embedded watermarks can successfully be recovered with high confidence if proper hiding codes are chosen.

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Data outsourcing can make data-integrity protection a challenging task, especially when the trustworthiness of a third-party is unproven. A novel auditing process for integrity verification of data stream, whose storage and handling is outsourced to a third-party, is explored. For this purpose, the hidden information within this data that provides support for in-network data aggregation environments, such as sensor networks is masked. This mask is achieved by simultaneous embedding of several shifted watermark patterns into multiple data streams. The nature of this watermark allows it to be aggregated into a single data stream with minimal loss of this information. A great benefit of the proposed scheme is that the embedded watermarks are invariant to linear time-sequential or cross-stream aggregation operations, such as summation or averaging. Therefore, multiple data streams can be merged and at the same time, and the existence of each individual watermark within allowable bounds can still be verified. The simulation results show that the embedded watermarks can successfully be recovered with high confidence if proper hiding codes are chosen.

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