Reliable protection for user data: retail and jamming

introduction

In the digital age, the size of the user data that is processed grows at an engineering rate, as well as the risks associated with leaks or unauthorized access. An effective approach to protecting this data is Retail (For passwords and certain types of secret information) and Jamming (Or encryption) When sending and storing data in juvenile flows.

Without these measures, the system can suffer not only from direct piracy but also from unintended leaks, for example, when the records are sent to a public warehouse or when the access settings are formed to Kafka.

In this article, we will explore the most common algorithms, how they look at Go, and why these steps are of special attention in information security.

TL; D

• Retail It provides passwords and personal data, which makes them not recovered for attackers.

• Jamming (Or encryption) It protects information about the flow of events (Kafka), which prevents third parties from reading them in the case of leaks.

• Cafka testimonies It is clearly determined Access to roles Enhancing security and avoiding accidental leaks in the console or consumer group.

• After the golden standard rules (A separate database for each service, deportation of any adjustments, and safe storage of passwords, keys, etc.) makes the system more flexible for electronic attacks.

The main section

1. Data division in the database

Why is it important?

1. Safety in the event of a compromise of the database: If someone gets access to your database (by throwing it or obtaining root privileges), retail passwords or other sensitive information remains unimapable in the normal text model.

2. Brand protection and reputation: Leaking retail data without the ability to recover original data much less than the company’s reputation from the leakage of passwords in an ordinary text.

Commonly used algorithms

• Bcrypt: Designed specifically for passwords; It contains a customized “cost” teacher that hinders brute force attempts by increasing the arithmetic complexity.

A short example (BCRYPT)

package main

import (
    "fmt"
    "golang.org/x/crypto/bcrypt"
)

func hashPassword(password string) (string, error) {
    hash, err := bcrypt.GenerateFromPassword([]byte(password), bcrypt.DefaultCost)
    if err != nil {
        return "", err
    }
    return string(hash), nil
}

• hashPassword It generates a fragmentation with recommended complexity settings.

Example, for example (Sha-256)

package main

import (
    "crypto/sha256"
    "encoding/hex"
)

func hashData(data string) string {
    hash := sha256.Sum256([]byte(data))
    return hex.EncodeToString(hash[:])
}

This method creates a strong fragmentation in a healing point of view. It is used for other types of data that is not necessarily related to passwords.

2. Jaming (encryption) in data flow

When the system creates events (for example, by identifying resources) for marketing or analyzes and sending them to Kafka (or other flow service), there is a risk that these messages may contain personal user data (email addresses, phone numbers, etc.). Jamming It helps to ensure that this data is not accessed in an ordinary text.

Case use: Marketing and user data

• Marketing needs data to divide the audience or evaluate the user’s behavior.

• Each user event (registration, clicks, purchases, etc.) is sent to Kafka.

• If the events contain non -encrypted personal data, an accidental leakage, or a “Rogue” that connects to the subject, it may lead to severe consequences.

Best practices:

1. Try the load load With a strong algorithm, such as AES.

2. Use Cafka certificates To enable safe authentication and encryption on the wire.

3. Restriction of access To the ACLS (access control lists) and roles -based policies.

A short example (AES)

package main

import (
    "crypto/aes"
    "crypto/cipher"
    "crypto/rand"
    "encoding/base64"
    "fmt"
    "io"
)

func encryptAES(plaintext, key string) (string, error) {
    block, err := aes.NewCipher([]byte(key))
    if err != nil {
        return "", err
    }
    
    cipherText := make([]byte, aes.BlockSize+len(plaintext))
    iv := cipherText[:aes.BlockSize]

    if _, err := io.ReadFull(rand.Reader, iv); err != nil {
        return "", err
    }

    stream := cipher.NewCFBEncrypter(block, iv)
    stream.XORKeyStream(cipherText[aes.BlockSize:], []byte(plaintext))

    return base64.URLEncoding.EncodeToString(cipherText), nil
}

func main() {
    key := "myVerySecretKey12" // AES-128/256
    data := "User Email: [email protected]"

    encrypted, err := encryptAES(data, key)
    if err != nil {
        panic(err)
    }

    fmt.Println("Encrypted Data:", encrypted)
}

Thus, even if someone gets access to a message, they will not be able to decode its contents without the key.

3. Expectations and additional measures

Although it is decisive, some business scenarios require unlimited data for analyzes (for example, age or country). In these cases, you need:

• Remove personal fields (Full name, exact email, phone number), and replace it with unidentified identifiers.

• Use certificates and ratification in Kafka So that no person without sufficient privileges can “join” to the consumer group or read the messages directly from the console.

A form of a safe access to the topic:

kafka-console-consumer --bootstrap-server kafka:9092 \
  --topic user-events \
  --consumer.config client-ssl.properties

the client-ssl.properties The file will contain the necessary certificate and the main information of the authentication.

conclusion

By combining retail and jamming, as well as forming access roles and properly deportations, you can achieve a multi -layer defense for user data. This not only reduces the potential risks of leaks but also enhances confidence and organizational in your system.

Standard gold bases

1. Database for each service: Each Microsercice service must have its own database to isolate the violations and prevent the unwanted service from reaching it.

2. Each user has a unique account: There are no joint accounts or \ u2014 passwords, and this simplifies the auditing and accountability paths.

3. Access to clearly defined roles by politics: All data operations should be subject to pre -defined rules (who can read, who can modify, etc.).

4. All database changes through deportation only: Avoid the arbitrary manual adjustments of the database scheme. Provides transparency, version and control.

5. All passwords in K8S cellars or secrets (including “salt” to decompose): You should never store sensitive data in the code or exposed in GIT. Use specialized solutions (Hashicorp Vauult, Kubernetes Secrets, etc.).

By following these recommendations, you greatly enhance security in your infrastructure and reduce the financial and reputation risks when dealing with user data.