The explosive data growth in high-performance computing (HPC) puts pressure on systems to process huge amounts of data and poses threats to the security of important data during transmission. Traditional data protection methods such as encryption inevitably attracts intermediate intercepting entities' attention. As a means of hiding information within other irrelevant data (carrier), steganography is used to transmit critical data without arousing the attention of regulators. Error-bounded lossy compression is a data reduction technique that effectively alleviates system pressures due to large data volumes. In this paper, we propose a steganography scheme based on the lossy compressor SZ named StegaZ. StegaZ performs steganography while compressing data by selecting random bits for insertion based on the password entered by the user. StegaZ does not affect unaware users' normal usage of the decompressed dataset. The experimental results show StegaZ preserves more than 99.6% of the original compression ratio and achieves a PSNR of 100% when selecting an appropriate dataset. Additionally, it imposes minimal compression bandwidth overhead, sometimes even able to obtain a higher compression bandwidth than the original.

Due to the increasing scale of scientific research, scientists need to collect massive amounts of data to solve complex scientific problems. The exponential growth of data poses significant challenges to high-performance computing (HPC) systems in terms of their computational ability, storage capacity, and transmission bandwidth. Data reduction techniques such as data compression have become one of the most promising solutions to these problems. Error-bounded lossy compression is now commonly utilized in HPC systems to substantially reduce data volume while precisely maintaining data accuracy. However, the majority of research was done on improving compression efficiency, such as compression ratio, and insufficient attention is paid to the security of the compression process.In this paper, we concentrate on the impact of corruption on error-bounded lossy compressor SZ, including corruption due to transient failures of hardware and corruption injected by malicious users. We analyze and quantify the influence of this corruption on compressed datasets by simulating the corruption errors that occur in the regression coefficient values and computation during compression using four failure models. The results demonstrate that SZ’s prediction-based design makes it sensitive to corruption of the regression coefficients. A single bit-flip in the regression coefficients can result in noticeable error propagation, in some cases, the compression ratio fluctuates up to 0.28%, but peak signal-to-noise ratio(PSNR) drops to negative levels.