A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models

The Private Equity (PE) firms operate investment funds by acquiring and managing companies to achieve a high return upon selling. Many PE funds are thematic, meaning investment professionals aim to identify trends by covering as many industry sectors as possible, and picking promising companies within these sectors. So, inferring sectors for companies is critical to the success of thematic PE funds. In this work, we standardize the sector framework and discuss the typical challenges; we then introduce our sector inference system addressing these challenges. Specifically, our system is built on a medium-sized generative language model, finetuned with a prompt + model tuning procedure. The deployed model demonstrates a superior performance than the common baselines. The system has been serving many PE professionals for over a year, showing great scalability to data volume and adaptability to any change in sector framework and/or annotation.Comment: Accepted by FinNLP (Financial Technology and Natural Language Processing) @ IJCAI2023 as long paper (8 pages and 8 figures

A practical comparison between algebraic and statistical attacks on the lightweight cipher SIMON

In the summer of 2013 NSA released a new family of lightweight block ciphers called SIMON. However they did not publish any assessment of the security of SIMON. Since then only a few papers on this topic have been released and none of them have included an algebraic analysis. Moreover only one paper described a practical implementation of the attack. This master thesis aims to implement a practical attack, both algebraic and differential, on SIMON. In doing so we are able to make a comparison between the two different attack methods. The algebraic attack was executed with SAT-solver CryptoMiniSat2 and could break 7 rounds. The differential attack was implemented in three steps. First we created a difference distribution table (DDT) and then we identified a differential by a search algorithm for the DDT. In the last step we designed a key recovery attack to recover the last round key. The attack could break 13 rounds for a 9 round differential. With a simple expansion on the key recovery attack it has the potential to break even more rounds for the same 9 round differential. This indicate that algebraic cryptanalysis might not be such a strong tool since it could only break 7 rounds. Furthermore, if a generic algebraic attack does not work on SIMON it has little or no chance of being successful on a more complex cipher. In other words this algebraic attack may serve as a benchmark for the efficiency of generic algebraic attacks

A practical comparison between algebraic and statistical attacks on the lightweight cipher SIMON

In the summer of 2013 NSA released a new family of lightweight block ciphers called SIMON. However they did not publish any assessment of the security of SIMON. Since then only a few papers on this topic have been released and none of them have included an algebraic analysis. Moreover only one paper described a practical implementation of the attack. This master thesis aims to implement a practical attack, both algebraic and differential, on SIMON. In doing so we are able to make a comparison between the two different attack methods. The algebraic attack was executed with SAT-solver CryptoMiniSat2 and could break 7 rounds. The differential attack was implemented in three steps. First we created a difference distribution table (DDT) and then we identified a differential by a search algorithm for the DDT. In the last step we designed a key recovery attack to recover the last round key. The attack could break 13 rounds for a 9 round differential. With a simple expansion on the key recovery attack it has the potential to break even more rounds for the same 9 round differential. This indicate that algebraic cryptanalysis might not be such a strong tool since it could only break 7 rounds. Furthermore, if a generic algebraic attack does not work on SIMON it has little or no chance of being successful on a more complex cipher. In other words this algebraic attack may serve as a benchmark for the efficiency of generic algebraic attacks

A practical comparison between algebraic and statistical attacks on the lightweight cipher SIMON

In the summer of 2013 NSA released a new family of lightweight block ciphers called SIMON. However they did not publish any assessment of the security of SIMON. Since then only a few papers on this topic have been released and none of them have included an algebraic analysis. Moreover only one paper described a practical implementation of the attack. This master thesis aims to implement a practical attack, both algebraic and differential, on SIMON. In doing so we are able to make a comparison between the two different attack methods. The algebraic attack was executed with SAT-solver CryptoMiniSat2 and could break 7 rounds. The differential attack was implemented in three steps. First we created a difference distribution table (DDT) and then we identified a differential by a search algorithm for the DDT. In the last step we designed a key recovery attack to recover the last round key. The attack could break 13 rounds for a 9 round differential. With a simple expansion on the key recovery attack it has the potential to break even more rounds for the same 9 round differential. This indicate that algebraic cryptanalysis might not be such a strong tool since it could only break 7 rounds. Furthermore, if a generic algebraic attack does not work on SIMON it has little or no chance of being successful on a more complex cipher. In other words this algebraic attack may serve as a benchmark for the efficiency of generic algebraic attacks