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.
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.
(1) Background: This study tracked the reporting of obesity in the Australian news media over three decades and how changing representations over time were linked to obesity-related public health policy developments. (2) Methods: Machine learning and computational language analysis techniques (word embedding, dichotomous bias mapping) were used to identify language biases associated with obesity in 157,237 relevant articles drawn from the Australian Dow Jones digital database of print news media articles from 1990 to 2019. (3) Results: Obesity-related terms were stigmatised on four key dimensions (gender, health, socioeconomic status, stereotypes), with language biased towards femininity and lower socioeconomic status in particular. Biases remained relatively steady from 2005 to 2019, despite recent policy initiatives directly seeking to address obesity stigma. To some degree, for each of the four dimensions, cosine values moved toward 0 over time (i.e., no association with one dimension poll or the other), but remained around 0.20. There was a strong relationship between news media and public health policy discourse over the 30-year study period. (4) Conclusions: With increasing recognition of the health consequences of weight stigma, policymakers and the media must work together to ensure public weight management narratives avoid discourse that may stigmatise heavier individuals, particularly women, and/or reinforce negative obesity stereotypes.
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