# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 1 Introduction  
At present, the exchange of data and information in the Internet network has increased very dramatically. With more and more people accessing the Internet and more and more content that can be accessed, the size of the data accessed in a given time increases on an exponential scale. With the increase in data access, more and more crimes related to data include data falsification, data theft, claiming unilateral ownership of data, leaking data, deception of data, and many other crimes related to Internet data access. These problems have implications for the more losses experienced by data owners, which also affect state losses. Losses suffered by the state cause harm to its people, such that crime in the Internet world only benefits certain parties and results in a big loss for the wider community. Thus, technology that provides security for data, including marking ownership rights to data and hiding important data when sent over the Internet, becomes mandatory to anticipate losses suffered by the wider community. With more and more data content accessed, the greater the memory capacity needed; besides, assuming the network infrastructure does not increase, the network capacity also decreases due to increased data traffic accessed, and power requirements on the network infrastructure also increase. These conditions bring problems in how to access data efficiently so that we can save the infrastructure and energy needs by minimum usage. One technique that can provide solutions to these problems isCompressive Sampling or Compressed Sensing (CS). This technique takes or picks up part of the data or signal from the sensor and then sends the data from the sample, and the receiver can reconstruct it back to the data as if it were authentic. In this paper, we propose a technique for sampling audio signals and inserting or hiding data into them at the same time, so that the sampled signals have a smaller size and, at the same time, there is data inserted into the encoded data. With this technique, the signal stored in the cloud system from recording results by sampling is smaller in size, and we can mark it with hidden data at the same time. The broadcast monitoring application is an example of how to monitor signals in real-time and store the results in the cloud. Monitoring such signals is more efficient if partial signal sampling is applied, such that the signal size becomes smaller than the original signal. At the same time, marking or indexing is applied by hiding data on the signal at any given duration to secure the authenticity of the monitored signal or to index the monitored signal by hiding its index on the encoded signal. Another application example is the recording of biomedical signals in which one samples them using several sensors and, at the same time, embeds the ownership marking or index into the encoded signal. Thus, the recorded biomedical signal has a smaller size than the original size, but does not reduce the quality of the biomedical signal, and there is a marking that is inserted in the encoded signal to secure the biomedical signal. CS in audio combined with the data hiding technique is a rare topic. The combination of CS and data hiding makes it possible to compress the audio and, at the same time, hide the watermark. [PERSON] in [1] and [PERSON] in [2] formerly proposed the CS applications in audio combined with data hiding. In [2], [PERSON] proposed an embedding method on host audio that was semi-fragile zero-watermarking by decomposing the audio into the wavelet domain and applying the CS technique to the audio wavelet coefficients without describing the audio reconstruction to determine the audio quality after the embedding process. Watermarks inserted in the measurement vector utilize positive and negative signs on the matrix elements. The result is that the inserted watermark is resistant to damaging samples from the signal. However, this paper does not explain the function of CS in terms of reducing the signal size.