## 4 Results
### Future Improvements and Challenges
Leakage detection and exploitation are substantial contributors, according to recent studies [174]. They were also noted as a critical factor in the failure of a data mining program. Researchers address the use of giveaway characteristics in data mining competitions to forecast the objective in another paper. This is because certain qualities were added afterward [175]. This article looks at the most popular techniques for categorizing documents and possible dataset structures for binary prediction. Each observation was given a \"legitimacy tag\" during the data collection phase, and data breaches were subsequently identified using a learn-predict separation. Maximum accuracy values of 91.2% for naive Bayes, 87.5% for k-NN, and 94.4% for centroid suggest that the suggested strategy is effective based on numerous categories. The use of EDA to detect leaks is an exciting area for future research since it can be used in various situations when the machine learning scientist has little control over the data-gathering procedure. Homomorphic Encryption (HE) is regarded as a significant technological achievement by many cryptography experts [176]. HE gives unreliable third-party access to private data without disclosing anything. The encrypted data may end up on the user's computer or an unauthorized distant server, but not the decryption key. No information will be disclosed to unauthorized parties so the host can relax. HE can be used for various things, including cloud computing, financial transactions, and defense against quantum computing. HE can be used in a constrained or expansive way. During machine learning training, Fully Homomorphic Encryption (FHE) aids in maintaining the confidentiality of sensitive data. Shallow machine learning and deep learning substantially rely on domain data, which isn't always easy to come by for free [177]. Asymmetric encryption techniques were first thought to be straightforward for quantum computers to decrypt [178]. A pair of keys--one public and one private--are used for asymmetric key encryption. By multiplying two huge prime numbers, these keys are created. We employ large encryption keys to ensure the security of our data because it is challenging to factor in large prime numbers but simple to factor in small ones. A more labor-intensive approach for factoring such enormous prime integers is Shor's algorithm. The superposition quantum computing method may allow the factors to be discovered in a small portion of the time needed by a traditional binary computer. Elliptic curve algorithms and digital signature technologies like RSA and DES are weak points. Grover's method [73], based on quantum computing, claims that it will only take 185 searches to find the key to a 56-bit DES. Despite the existence of quantum computers, symmetric essential techniques like AES are still secure. Researchers are examining if these constraints may be overcome using mathematical and quantum methods. A quantum key distribution example is the BB84 protocol [179]. Lattice-based cryptography and other mathematical techniques are also being researched. Asymmetric encryption cannot be solved with quantum computing; however, using it as subroutine helps speed up machine learning [180]. This can drastically shorten prediction times for algorithms like SVM, where constructing a hyperplane and performing kernel modifications can take some time. They may also be used for deep learning if they are well-designed. However, there are problems because quantum neural networks move in a straight path.