In this article, we propose an instantiation, called${\sf Stanislas}$, of a dedicated Self-Synchronizing Stream Cipher (SSSC) involving an automaton with finite input memory using non-triangular state transition functions. Previous existing SSSC are based on automata with shifts or triangular functions ($T$–functions) as state transition functions. Our algorithm${\sf Stanislas}$admits a matrix representation deduced from a general and systematic methodology called Linear Parameter Varying (LPV). This particular representation comes from the automatic theory and from a special property of dynamical systems called flatness. Hardware implementations and comparisons with some state-of-the-art stream ciphers on Xilinx FPGAs are presented. It turns out that${\sf Stanislas}$provides bigger throughput than the considered stream ciphers (synchronous and self-synchronizing) when straightforward implementations are considered. Moreover, its synchronization delay is much smaller than the SSSC Moustique (40 clock cycles instead of 105) and the standard approach CFB1-AES128 (40 clock cycles instead of 128).

Internet users are increasingly concerned about their privacy and are looking for ways to protect their data. Additionally, they may rightly fear that companies extract information about them from their online behavior. The so-called tokenization process allows for the use of trusted third-party managed temporary identities, from which no personal data about the user can be inferred. We consider in this paper tokenization systems allowing a customer to hide their credit card number from a webshop. We present here a method for managing tokens in RAM using a table. We refer to our approach as upcycling as it allows for regenerating used tokens by maintaining a table of currently valid tokens. We compare our approach to existing ones and analyze its security. Contrary to the main existing system (Voltage), our table does not increase in size nor slow down over time. The approach we propose satisfies the common specifications of the domain. It is validated by measurements from an implementation. By reaching 70 thousand tries per timeframe, we almost exhaust the possibilities of the 8-digit model for properly dimensioned systems.