Abstract User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users’ data publishing because some attributes may expose multiple users’ identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users’ communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users’ community can occur from the privacy preserved published data. Each item of user attributes impacts users’ community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users’ unique identifications and sensitive information inferences more easily, and their presence in published data increases users’ community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user’s community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users’ community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user’s dataset to effectively preserve users’ community privacy. Data generalization is performed adaptively by considering both user attributes’ susceptibility and entropy simultaneously. The proposed algorithm controls over-generalization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users’ community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

open access article

This contribution is focused on self-routing permutation networks capable of routing all n! permutations of its n inputs to its n outputs without internal conflict. First, a self-routing permutation network named BNB SRPN is described. The network realizes the self-routing capability on the structure of the generalized baseline network, a modified model of the original baseline network. The network reduced both the hardware and the delay time compared with other comparable networks by a simple algorithm using 1-bit information for the routing decision. The network also has a good hardware regularity. In addition, a cost-effective self-routing network is presented, which is derived from the BNB SRPN. The network's hardware complexity, O(N log N), is the same as that of the Benes network which is not self-routing. The principle realizing a modular structure is also presented. The modular structure is derived from the principle to localize the routing decision. It is shown that the modular structure results in the reduction of the total delay through the network.>

A self-routing permutation network capable of routing all n factorial permutations of its n inputs to its n outputs is presented. The network implements the binary radix sorting on the structure of the generalized baseline network, a modified model of the original baseline network. The network has O(N log/sup 3/ N) hardware complexity and O(log/sup 3/ N) delay time for N-inputs. The network makes use of the localized bit information instead of the global information in routing procedure. This strategy leads to the reduction of both the hardware and the delay time compared with other comparable networks. The resulting hardware is simple, and has a good regularity.>