## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.3 Dimension III: Data Privacy and Security  
The use of personal or sensitive data in AI can raise concerns regarding privacy and security. It is important to ensure that the data are stored and processed securely and that privacy regulations are followed. To address this challenge, businesses should implement data privacy and security policies and procedures such as data encryption and access control. Data Privacy Challenges in AI  
Data Collection and Sharing: AI systems require large quantities of data to train effectively, often leading organizations to aggregate data from various sources, potentially exposing sensitive user information [98]. Data-sharing agreements and collaborative data analysis projects can exacerbate these concerns, especially when data are shared across international borders with differing privacy regulations [99]. Inference Attacks: AI models can inadvertently reveal sensitive information about training data, even when the data are anonymized [100]. For example, attackers can use model inversion or membership inference attacks to extract private information from a model's predictions or to learn whether a specific data point is included in the training set [101]. Differential Privacy: Differential privacy (DP) is a popular approach for preserving privacy during data analysis by adding controlled noise to the data [102]. Although DP provides strong privacy guarantees, it can be challenging to implement in practice, especially when balancing privacy protections and model utility [103]. Data Security Challenges in AI  
Adversarial Attacks: Adversarial attacks, in which small perturbations are introduced to input data to deceive AI models, pose significant security risks [104]. These attacks can lead to incorrect predictions or classifications, undermining the reliability of AI systems in critical applications, such as healthcare, finance, and autonomous vehicles [105]. Data Poisoning: Data poisoning attacks involve tampering with training data to degrade the performance of an AI model [106]. These attacks can be difficult to detect because they often target a small subset of the training data and require minimal modifications to the poisoned data points [107]. Figure 10: Challenges’ elements of data privacy and security. Model and Data Tampering: Attackers can also target AI models and data directly by altering model parameters, weights, or the data itself [108]. Techniques such as backdoor attacks or Trojan neural networks can introduce hidden malicious behavior into AI models, thereby posing significant security risks [109]. In backdoor attacks, an attacker injects malicious code into the model during the training process, causing the model to produce incorrect outputs or exhibit unintended behavior when triggered by a specific input pattern [110]. Trojan neural networks, on the other hand, involve embedding a hidden trigger within the model, which, when activated by a specific input, causes the model to perform unauthorized actions or provide incorrect predictions [111]. Model extraction attacks involve model tampering, in which an attacker seeks to replicate a target model by querying it and learning from the responses [112]. This can lead to intellectual property theft, or even the creation of duplicate models with malicious intent [113]. To defend against model and data tampering, organizations can employ various strategies such as model hardening, secure model storage, and input validation. Model hardening techniques, such as fine pruning, can help remove malicious components from a model while maintaining its overall performance [114]. Secure model storage using encryption and access controls can protect a model from unauthorized modifications [115]. Input validation can be employed to ensure that only legitimate inputs are processed by the AI system, mitigating the risk of triggering hidden backdoors or Trojan networks [116].