Abstract: Disclosed is a data encryption method performed by an apparatus, which includes encrypting plaintext data based on an encryption key to generate first ciphertext data, applying a noise vector being periodically extracted to an artificial intelligence-based generative model to generate a first signature code and a second signature code, and applying the first signature code and the second signature code to the first ciphertext data to generate second ciphertext data. The generating of the first signature code includes determining a type and a replacement location of a character necessary to generate the first signature code by means of a predetermined conversion formula and generating a first character, which is obtained by calculating an existing encryption character being present at the replacement location in the first ciphertext data and the character in a predetermined scheme, as the first signature code.

Abstract: Disclosed are a personal information detection reinforcement method using multiple filtering and a personal information detection reinforcement apparatus using the same. The personal information detection reinforcement method includes performing first filtering of input data using record data and pattern data, classifying a class of the first-filtered input data using a previously constructed supervised learning model, performing second filtering of the first-filtered input data using an unsupervised-based algorithm based on the classified class, and updating the supervised learning model based on the second-filtered result data.

Abstract: Provided are an apparatus and method for generating random numbers based on reinforcement learning. The method is implemented with a computer, and includes: generating a first random number based on a deep learning-based first model through an agent unit; calculating respective similarities between at least one previously stored second random number and the first random number; performing reinforcement learning based on generative behavior of the first random number of the agent unit based on the respective similarities; and storing the first random number in a memory. The performance of the reinforcement learning is configured to provide compensation information based on the respective similarities to the agent unit through an environment unit, and then to control the agent unit to learn the first model based on the first random number.

Abstract: A data encryption/decryption method including a web server, a web application server (WAS) receiving data, a database server (DB server) receiving a structured query language (SQL), and an encryption/decryption server to the WAS through a network, the data being transmitted between the encryption/decryption server and the WAS by an application repeater, the method includes performing encryption by filtering an encryption target by using a uniform resource identifier (URI) when the data is transmitted from the web server and transmitting data to be encrypted to the encryption/decryption server, and performing decryption by filtering a decryption target by using the SQL input from the WAS to the DB server and transmitting data to be decrypted to the encryption/decryption server.

Abstract: A data encryption/decryption method including a web server, a web application server (WAS) receiving data, a database server (DB server) receiving a structured query language (SQL), and an encryption/decryption server to the WAS through a network, the data being transmitted between the encryption/decryption server and the WAS by an application repeater, the method includes performing encryption by filtering an encryption target by using a uniform resource identifier (URI) when the data is transmitted from the web server and transmitting data to be encrypted to the encryption/decryption server, and performing decryption by filtering a decryption target by using the SQL input from the WAS to the DB server and transmitting data to be decrypted to the encryption/decryption server.