Foreword to the Special Issue of the 8th International Conference on Applications and Techniques for Information Security (ATIS 2017)

Abstract

In this data-driven era, the world has profoundly benefitted from the advanced information and communication technologies for the purposes spanning from data collecting, data storing, and data processing to data analyzing and data publishing. However, the increasing severe threats and attacks toward information abuse in various critical cyberspace areas have also caused significant concerns. This urgency forms a timely request for advanced, formal, and general methodologies and techniques for information abuse prevention. In the context of cyberspace, the information abuse behaviors could include revealing the private information of individuals or the trade secret of businesses by the willful or negligent analysis activity. These behaviors are particularly vital for systems involving highly sensitive information, such as in accounting and financing context. While our previous special issue in CCPE (New advances in securing cyberspace and curbing crowdturfing) has discussed the related security issues and solutions regarding crowdturfing in a high-level background of cyberspace, in this special issue, we continue and aim to provide the readers with timely and fundamental information abuse prevention techniques under the theme of cybersecurity and privacy. Based on the submissions in the 8th International Conference on Applications and Techniques for Information Security (ATIS 2017) and an open call for papers procedure, we selected three representative research articles for publication after rigorous peer-review processes. These articles proposed secure and private solutions that are focusing on the architectural level and the data service level in the areas of cyberspace. Here, we provide an integrative perspective of this special issue by summarizing each contribution contained therein. The hybrid architecture–based clusters, which involve ARM CPUs and FPGA fabric, are playing an essential role in accelerating the encryption/decryption of massive data. The paper ‘‘A hybrid ARM-FPGA cluster for cryptographic algorithm acceleration’’ employed a 48-node cluster infrastructure based on the Xilinx Zynq SoC to accelerate classical cryptographic algorithms, including hash functions, AES, and RSA.1 During the design, the authors leveraged the flexibility of the software to implement node-to-node communication through the Message Passing Interface (MPI) and offloaded the compute-intensive tasks to the FPGA to accelerate complex calculations with the parallelizability of specific reconfigurable coprocessors. The authors also studied several parallel cryptography optimizations based on FPGA to evaluate this cluster. The efficiency of the implementations of the selected data encryption and decryption algorithms demonstrated the performance of the proposed system. Collaborative private data analysis aims to allow inference to be drawn on the joint data without disclosing private data held by each party with executing privacy-preserving statistical algorithms. The paper ‘‘PPEM: privacy-preserving EM learning for mixture models’’ presented a privacy-preserving expectation-maximization (PPEM) algorithm for carrying out maximum likelihood estimation of the parameters of mixture models.2 By considering the scenario of horizontally partitioned data distributed among three or more parties, the PPEM algorithm is a two-cycle iterative distributed algorithm for fitting mixture models under privacy-preserving requirements and supports strong security assumptions. Besides, PPEM was applied to the normal mixture model (NMM) and t-mixture model (tMM), followed by a security analysis for effectiveness. A real data example was presented to evaluate the computational complexity and accuracy of PPEM relative to its non–privacy-preserving version. In the private data collection scenarios such as crowdsourcing, Jaccard similarity has been widely used to measure the distance between two preference profiles from two individuals. There is a new need for designing differentially private protocols in which the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved preference profiles but without being allowed to access them directly. The paper ‘‘Locally private Jaccard similarity estimation’’ addressed the above requirements by considering the local differential privacy model.3 To achieve this, the authors initially focused on a particular hash technique, MinHash, and designed the PrivMin algorithm by the Exponential mechanism to build the LDP-JSE protocol. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy. Although this special issue has provided several up-to-date research on information abuse prevention for cybersecurity and privacy, we should note that many other interesting areas such as its relationships with computational social choice and blockchain are also worthy of being explored in the future. Before the end of this editorial, we would like to thank the anonymous reviewers for their great efforts in reviewing the submitted manuscripts; without them, this special issue would not have been published with such high quality. We would like to thank the Office