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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. a https://orcid.

Rainbow tables are techniques commonly used in computer security to invert one-way functions, for instance to crack passwords, when the domain of definition is reasonably sized. This article explores the limit on the problem size that can be treated by rainbow tables when the precomputation and the attack phases are both CPU-driven. We conclude that the bottleneck is no longer the memory as it may have been and the precomputation phase seems to have been underestimated so far. We offer a comparison of what can be done on different environments depending on the needs and available computing power of the users.