Today's exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independent-block-wise model that decomposes the entire dataset into many independent sub-blocks to compress. Then, we design and implement a series of error detection/correction strategies elaboratively for each stage of SZ. Our method is arguably the first algorithm-based fault tolerance (ABFT) solution for lossy compression. Our proposed solution incurs negligible execution overhead in the fault-free situation. Upon soft errors happening, it ensures decompressed data strictly bounded within user's requirement with a very limited degradation of compression ratio and low overhead.
