## 4 Privacy-Aware Selection Modes
### Trust Model  
The social relationships between mobile users are the main element that defines the meaning of privacy; they can vary from weak to strong ties depending on the context or the location of the opportunistic network. Variant social metrics can determine this relationship, including profiling similarity, number of encounters, number of common friends, distance count less than six in the six-degree theory, centrality, etc. Considering \(n\) common countable social metrics between two nodes \(A\) and \(B\), we calculate the trust between two nodes \(A\) and \(B\) by the following weighted equation:  
\[Trust(A,B)=\sum_{i=1}^{n}\biggl{(}\alpha_{i}\frac{\text{Metric}_{i}(A,B)}{ \max(\text{Metric}_{i}(A,*))}\biggr{)}\,where\sum_{i=1}^{n}\alpha_{i}=1 \tag{1}\]  
Equation (1) states that the trust score is calculated with \(n\) social metrics; for each metric and node, we evaluate this metric for its neighbors (\(*\) nodes) (depending on the node coverage zone). When the node has no relation regarding that metric, the metric will not be considered. The node with the maximum value will take a full trust value and constitute the reference node for that metric. Accordingly, any node will be rated according to that reference node for that specific metric. The total score will be weighted by \(\alpha_{i}\) to combine the metrics and tune their impact on the trust score.  
In this paper, we experiment with the trust score for two metrics:1. Number of Common Friends (NCF)--counts how many common friends two nodes have. Higher NCF indicates a stronger social tie. The value ranges from 0 (no common friends) to (network size--2) (maximum possible common friends).
2. Number of Exchanged Messages (NEM)--counts previous messages between two nodes. Higher NEM indicates a better relationship. Value is 0 for no prior exchanges or a positive number x for x messages exchanged in the past. Then, the trust scores using only the _NCF_ metric, only the _NEM_ metric, or the _NCF_ and _NEM_ metrics jointly between two nodes \(A\) and \(B\) are calculated, respectively: \[Trust(A,B)=\frac{NCF(A,B)}{max(NCF(A,*))}\] (2) \[Trust(A,B)=\frac{NEM(A,B)}{max(NEM(A,*))}\] (3) \[Trust(A,B)=\alpha_{1}\frac{NCF(A,B)}{max(NCF(A,*))}+\alpha_{2}\frac{NEM(A,B)} {max(NEM(A,*))}\] (4)  
\(\alpha_{1}\) and \(\alpha_{2}\) are weights in [0; 1; 2] to tune metric impacts, \(\alpha_{1}+\alpha_{2}=1\), and the trust score range is [0; 1; 2].  
The calculated score represents the tie strength between two nodes. We note firstly that the trust scores are directional--node A's score for B may differ from B's score for A. Secondly, a higher score indicates stronger trust/familiarity, and thirdly, thresholds are defined to classify relationships as stranger/weak tie/familiar. When we use one single metric approach, we simplify the trust calculation, while the combined metrics provide more fine-grained scoring to distinguish different relationship strengths. Both help nodes determine social trust with peers during routing. Using this score, we define three relationship strength levels using thresholds: (1) stranger, (2) weak tie (acquaintance), and (3) familiar. Therefore, a higher score means stronger social ties and higher trust between nodes and vice versa. The thresholds help qualify trust levels like strangers, acquaintances, and close friends. Each node stores NCF and NEM values for every other node locally and updates them continually. This information is used during message forwarding to pick trusted next hops and enable private routing.