# Reputation-Based Trust Approaches in Named Data Networking
## 4 Design Options
On the other hand, there are decentralized approaches, whereby ratings are calculated by each node independently and then may be (optionally) distributed between the nodes by a dissemination protocol, in a P2P fashion. This approach eliminates the single point of failure problem, but requires that the individual nodes performing the calculations and providing the reputation values can be trusted, as well. To solve these problems, blockchain technology could be integrated in NDN as a solution, as it offers both the trustworthiness a central authority would (e.g., by recording ratings in the ledger, making them immutable and verifiable), combined with the distributed announcement of reputation values. Figure 2: Centralized vs Decentralized: (**A**) Centralized: (**a**) Receiving content & assigning ratings, (**b**) Sending ratings to a CA, and (**c**) CA computing & announcing reputation value. (**B**) Decentralized: (**a**) Receiving content & assigning ratings, (**b**) Exchanging ratings, and (**c**) Computing reputation value individually. **Transaction-based versus Opinion-based trust (Figure 3):** The former relies on objective information derived from transactions. Verification of data integrity and provenance authentication can be considered as trust metrics, as they are supplied by NDN's default trust management scheme. Alternatively, networking parameters such as delay could as well be a viable option. The latter, opinion-based trust, is based on the subjective opinion of an entity for another based on their past interactions. Although opinion-based mechanisms come with a lower overhead, they are more vulnerable to attacks. As a result, transaction-based schemes are preferred in security-related domains while opinion-based are most suitable when the main objective is data quality assessment. **Global reputation versus Local reputation (Figure 4):** Reputation can either be seen as a global property shared by all, or as a subjective property assessed locally by each entity. The localized approach is privacy-preserving and more suitable for decentralized environments. However, the global approach produces more accurate results as the calculation uses a more sufficient, representative amount of ratings. In NDN, global availability of reputation values can complement attack mitigation, (e.g., cache-poisoning). A local approach is more appropriate for assessing the quality of data. It is worth highlighting here that all aforementioned works propose decentralized mechanisms which compute trust locally. In addition, ratings are assigned based on information derived from completed transactions. In particular, some rely on exclusion information to provide ratings [22; 32], while others quantify the reputation values by evaluating the number of received poisoned copies for certain content [33], or by taking into account measurements of Interest [35] and exclusion [34] rates. **Rank versus Threshold based schemes (Figure 5):** Rank-based approaches return a relative ranking of all entities' reputation. NDN can thus prioritize content with higher quality or providers with higher reputation. Such an approach has been used to prioritize valid over fake content [22] or to favor the most trustworthy routers to be selected as next hops during forwarding [33]. However, rank-based methods come with their drawbacks. For instance, high-ranked content is not always the best match for an Interest. In contrast, a threshold-based approach excludes entities which do not meet the desired criteria. Due to its strictness, it can be more efficient when leveraged to make forwarding and caching decisions [32; 35] or to detect an attack [34; 35]. **Incentive- versus Rule-based control (Figure 6):** A rule-based system forces entities to follow its rules (e.g., rating according to specified parameters). An incentive-based ones, motivates entities to follow desired behaviors (e.g., provide ratings).