  Remotely generated data by Intent of Things (IoT) has recently had a lot of
attention for their huge benefits such as efficient monitoring and risk
reduction. The transmitted streams usually consist of periodical streams (e.g.
activities) and highly private information (e.g. IDs). Despite the obvious
benefits, the concerns are the secrecy and the originality of the transferred
data. Surprisingly, although these concerns have been well studied for static
data, they have received only limited attention for streaming data. Therefore,
this paper introduces a new steganographic mechanism that provides (1) robust
privacy protection of secret information by concealing them arbitrarily in the
transported readings employing a random key, and (2) permanent proof of
originality for the normal streams. This model surpasses our previous works by
employing the Discrete Cosine Transform to expand the hiding capacity and
reduce complexity. The resultant distortion has been accurately measured at all
stages - the original, the stego, and the recovered forms - using a well-known
measurement matrix called Percentage Residual Difference (PRD). After thorough
experiments on three types of streams (i.e. chemical, environmental and smart
homes), it has been proven that the original streams have not been affected (<
1 %). Also, the mathematical analysis shows that the model has much lighter
(i.e. linear) computational complexity O(n) compared to existing work.
