No abstract

Over the last decade Online Social Networks (OSN) privacy has been thoroughly studied in many aspects. Some of these privacy related aspects are trust and credibility involving the OSN user-data conveyed by different relationships in the network. One of OSN major problems is that users expose their information in a manner thought to be relatively private, or even partially public, to unknown and possibly unwanted entities, such as adversaries, social bots, fake users, spammers or data-harvesters. Preventing this information leakage is the target of many OSN privacy models, such as Access Control, Relationship based models, Trust based models and many others. In this paper we suggest a new Role and Trust based Access Control model, denoted here as RTBAC, in which roles, that manifest different permissions, are assigned to the users connected to the Ego-node (the user sharing the information), and in addition, every user is evaluated trust wise by several criteria, such as total number of friends, age of user account, and friendship duration. An interesting extension of the model of image anonymization is also given, where a user that has a certain role with a proper permission can access a partial instance of the data, if a sufficient trust level is not achieved. These role and trust assessments provide more precise and viable information sharing decisions and enable better privacy control in the social network.

The subset sum problem (SSP) can be simply described as: given a set of integers A, and an integer s, find a subset of items from A summing up to s. To this classic problem there are many variants; one is the multiple-subset problem (MSSP) in which there is a selection of items from a given set to several identical bins, having each bin capacity not exceeded, and the total weight of the items is maximized. Here a related different kind of problem is approached: given a set of sets A = {A1,A2,…,An}, find an integer s, for which every subset of the given sets is summed up to, if such an integer exists. To this specific problem there are several parameters that are applicable: the size of each set-m, the number of sets-n, and the size of every integer (number of digits)-d. For equal sizes of these parameters, a cryptographic application is shown, in which the cipher text is the sequence of the given sets, the private keys are two of the parameters m, n, and d, and the encrypted plain text is the resulted sum s. As a variant of SSP, the problem is NPcomplete, and for known private keys, a relatively efficient algorithm is given, based on dispensing non-relevant values of the possible sums.

No abstract

No abstract

This research deals with the case of a smart intersection, where several cars approach the intersection from various directions, and a smart traffic light must decide about the time intervals of RED and GREEN in each direction, based not only on the number of vehicles in each lane, but also on other factors such as the type of vehicles (e.g. emergency vehicles), and the social characteristics of the passengers (e.g. a handicapped person, a student who is late for an exam). Those factors will be gleaned from the IoT (Internet of Things) network amongst cars, traffic lights, individuals, municipality data, and more. Once those priorities have been examined, they are fed into the algorithm we have devised, and outputted as a timing schedule for the different sides of the intersection. In this paper we present the algorithm, the prioritizing research, its implementation in the algorithm and initial results.

Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities.

No abstract