The attributes that could be considered sensitive for fairness in the context of the job application process described are:

1. `case:citizen`: Knowing whether individuals are citizens or not could have implications for protection against discrimination based on nationality, immigration status, or legal authority to work in a certain country. This information could potentially be used to make decisions that unfairly advantage citizens over non-citizens.

2. `case:gender`: Gender is a protected characteristic under many laws worldwide, as it cannot be a basis for discrimination in hiring processes. Knowledge of an applicant's sex or gender could lead to employment practices that violate fairness and equal opportunity laws. However, it is not shown in the frequency data provided, and might not be directly evident unless inferred from other attributes.

3. `case:german speaking`: Similarly, proficiency in a specific language like German could imply management or client requirements, but in itself, it is not necessarily sensitive. The context is crucial here. If the hiring targets certain markets or types of clients where German proficiency is necessary, then this attribute can become a possibly sensitive factor depending on how it's used in decision-making.

4. `concept:name`: Though it represents the events in the process, the frequency data for events like applications or interviews doesn't inherently contain sensitive information unless connected with other attributes. The common distinction made like hand-in job application would be part of work processes rather than sensitive data by itself.

5. `resource`: This attribute provides details about the people or tools involved in the process (like HR-dummies, coding interviewers, etc.). Knowing specific roles or individuals involved might aid the process or expose discriminatory practices but is not inherently sensitive unless these roles or individuals are linked with performance evaluations. As the frequency data suggests, these resources are used across the process events rather than indicating similarities or differences that would require sensitive information handling by themselves.

The fair treatment aspect would depend on how these sensitive attributes are used in conjunction with other factors to make decisions. For example, requiring a specific language proficiency could be fair if it justifies needing someone who can operate in that language critically. However, being able to determine citizenships, gender, and other sensitive characteristics could lead to potential unfair advantages or disadvantages - such as biases concerning race, nationality, or gender.

Lastly, whilst someone might have high performance data associated with being a Senior Partner or an HR-prof, using these distinctions for hiring decisions could also lead to hiring bias mechanisms without explicit intent. Transparency around how such data is used could help mitigate potential discrimination issues and uphold fair employment standards.