  In this paper, we address the problem of data reconstruction from
privacy-protected templates, based on recent concept of sparse ternary coding
with ambiguization (STCA). The STCA is a generalization of randomization
techniques which includes random projections, lossy quantization, and addition
of ambiguization noise to satisfy the privacy-utility trade-off requirements.
The theoretical privacy-preserving properties of STCA have been validated on
synthetic data. However, the applicability of STCA to real data and potential
threats linked to reconstruction based on recent deep reconstruction algorithms
are still open problems. Our results demonstrate that STCA still achieves the
claimed theoretical performance when facing deep reconstruction attacks for the
synthetic i.i.d. data, while for real images special measures are required to
guarantee proper protection of the templates.
