## 7 Conclusions  
An innovative, reliable, low-demand, and highly effective system for detecting conflicting consents and user consent policies regarding personal data, based on sophisticated computational methods, was presented in this work. The Intelligent Policies Analysis Mechanism (IPAM) implements sophisticated conflict recognition rules to assist the average user in making an optimal decision and protecting them from impending profiling, with computational intelligence methods. Specifically, using Fuzzy Cognitive Maps (FCMs), which is an advanced form of neuro-fuzzy decision support system, the protection of user privacy in the IoT ecosystem is modeled in the most efficient and intelligent way. Respectively, with the use of Extreme Learning Machines (ELMs), which are highly efficient neural systems of small and flexible architecture, which can operate optimally in complex environments, it is possible to identify the conflicting rules that may result in profiling. This proposed mechanism, which is an integral part of the ADVOCATE framework [12; 13; 14], greatly enhances its security mechanisms and is a promising intelligent mechanism for protecting the privacy of users whose personal data are the main target of modern cyberattack methods. It is important to note that the application of artificial intelligence to methods of controlling and protecting users' personal data significantly enhances the active security mechanisms of these methods and creates new perspectives on how to deal with cybercrime. It is also important to emphasize that the complexity of the IoT ecosystem, the uncertainty it brings, as well as the instability of other learning algorithms in such a dynamic environment, favor the adoption of a method that normalizes the noisy field and brings consistent results capable of modeling serious, multi-dimensional problems. On the other hand, literature points out how the side-channel information can be used to extract some of the sensitive data that the GDPR tries to protect [34]. For example, [PERSON] et al. [35] present a private routing protocol for anonymous communication between different networks (e.g., wireless sensor networks, etc.) using technologies such as Spatial Bloom Filters (SBF), tunneling, and homomorphic encryption. The proposed routing protocol preserves context privacy and prevents adversaries from discovering the network topology and structure, as routing information is encrypted and computed by performing calculations in the actual encrypted data. Consequently, the achieved privacy is vital in preventing adversaries from obtaining valuable network information from a successful attack on a single node of the network and reduces the likelihood of an escalation attack. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Classifier** & **TA** & **RMSE** & **PRE** & **REC** & **F-Score** & **ROC Area** & **Training Time** & **Validation** \\ \hline ELM & 99.87\% & 0.0353 & 0.999 & 0.999 & 0.999 & 0.999 & 1.1 s & 10-FCV \\ SVM & 97.92\% & 0.1441 & 0.980 & 0.979 & 0.979 & 0.980 & 14.9 s & 10-FCV \\ k-NN & 97.53\% & 0.2306 & 0.976 & 0.975 & 0.975 & 0.994 & 15.3 s & 10-FCV \\ Random Forest & 98.17\% & 0.1424 & 0.982 & 0.982 & 0.982 & 0.996 & 15.6 s & 10-FCV \\ \hline \end{tabular}
\end{table} 
Table 3: Algorithm comparison providing classification accuracy and performance metrics. Future study could include additional behavior analysis of the smart entertainment devices, in order to be improved by additional adjusting of the parameters of the suggested framework, so that an even more effective, precise, and faster classification process could be reached. Multi-format exemplifications may support a reporting system as part of a general decision framework. In addition, more sophisticated methods could be used for precise identification of contradictory and conflicting policies. Also, it would be essential to study the expansion of this method by applying the same architecture in a big data architecture framework like Hadoop [36].