The chain given to the user would do not contain any reference from a specific block to which the user has no access. Data encrypted has been uploaded to the blockchain and the method modifies the reference part of the chain to connect only the blocks to which the user has access. According to that, the service access data has been encrypted at the attribute level with a specific scheme and key. Based on the service level and data, the method selects an encryption scheme and key to perform data encryption. As the features are organized in multiple levels, the method maintains a set of schemes and keys for each level dedicative.
On the other side, the method maintains different encryption schemes and keys for each level of features. Based on the value of FLAG, the user has been granted or denied service access. At each level, the method checks the set of features being accessed and the number of features the user has access grant to compute the FLAG value for the user according to the profile given.
The RSFSA model analyses the sensitivity of different features being accessed by any service and at multiple levels. To challenge the adversaries with more sophisticated security measures, an efficient real-time service-centric feature sensitivity analysis (RSFSA) model is proposed in this paper.
However, the data security was enforced at the attribute level, the adversaries are capable of learning the method of data encryption even there are access restrictions are enforced at an attribute level. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.ĭata security in the cloud has become a dominant topic being discussed in recent times as the security of data in the cloud has been focused on by several researchers. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. It is more stable than methods based on wireless signals. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs).
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