CUADRO Nº 6 INDICES DE GINI POR COMUNIDADES AUTONOMAS.
IV. UNA APROXIMACION DE LOS EFECTOS REDISTRIBUTIVOS DEL GASTO PUBLICO POR NIVELES EDUCATIVOS.
8.1 Conclusion
The change of individuals’ sharing attitude over time presents a challenge of adaptation for location- based applications, especially for the ones which use static mechanisms. In this thesis, we first presented an optimization on algorithm in [6] that identifies optimal policy for a subject utilizing long- period training set. Four policies per subject is defined in different levels of strictness in order to simulate sharing preferences of individuals with diverse confidence intervals. Second, we presented routine aware control mechanism at decision-making process of location privacy applications, that detects probable false-positives caused by user-defined privacy preferences. The system is based on estimation of conditional trajectory entropy, and similar to approach proposed in [16]. In order to evaluate our approaches, we have used Reality Mining dataset [24] on which communication events are used as reference for sharing behaviour.During the evaluation phase, it was discovered that even policies those are trained aiming higher accuracy value rather than the most sharing, was not able to cover subjects’ sharing attitude in test set successfully. However, overall performance of routine aware control mechanism together with rules which have static nature, has implied improvement on prevention against false-positives. Further, in standalone evaluation, entropy systems had positive influence on approximately 60% of subjects. The results indicate that considering given dataset, entropy value of locations was more informative to distinguish false-positives and true-positives than the rules in policies, and entropy systems can play supportive role in location privacy mechanisms.
8.2 Future Work
As discussed in Chapter 4, using two A star algorithms with separate heuristic functions results in identifying not the best but the optimal policy for a subject. The reason is, heuristic function of first A star algorithm is able to measure cost in its own domain, but unable to measure in second algorithm’s domain. This implies that first ran tries to form clusters in ground-truth space as broad as possible, but do not consider if generated clusters would form the best policy given cluster limit in second algorithm. The future work on heuristic function may provide identification of best policy given rule limit using large-scale datasets.
Further, estimating entropy of a location by utilizing trajectory entropy of previously visited locations has a drawback which is, once trajectory is affected by surprise location or sequence of locations, system cannot accurately differentiate farther locations whether they are routine or out-of-routine events until path information is overwritten. To overcome this situation, system should able to recognize initial and future destination locations. Further research on probabilistic system which can predict destination location given time slot, and construction of a joint system together with entropy mechanism can restrain this influence.
Finally, another implementation may be considered together with routineness aware control mechanism and dynamic location privacy system adopts machine-learning algorithms. That being said, dynamic learning was not in the scope of this thesis. Further evaluations can be applied using dynamic learning approach with real-time markov chain updates, which can adapt mobility pattern changes (e.g. relocation or holiday situations) of subjects. On the other hand, the entropy systems with higher degree of detail (e.g. entropy systems exclusive for participant, area, and weekend or time) can be trained and tested mining larger datasets. Because individuals are prone to use different mobility