The association between organizational cynicism and organizational citizenship behavior : a case study

The continuation of business activities is possible with motivated and dedicated employees. In this sense, the notion that an organization is devoid of honesty and negative feelings regarding an organization are important because they tend to manifest as the display of abuse and criticism in line with such convictions and emotions and determine organizational citizenship behavior. The objective of this study is to determine the association between organizational cynicism attitudes and organizational citizenship behavior manifestations and corrective measures to be taken by administrators aware of the current status in their enterprises. Within this objective and target, a survey was implemented to the employees of a total of 637 five-star tourism enterprises operating in Antalya province and it has been determined that there is a significant difference between the levels of organizational cynicism and organizational citizenship behavior.peer-reviewe

SecFlow: Adaptive Security-Aware Workflow Management System in Multi-Cloud Environments

In this paper, we propose an architecture for a security-aware workflow management system (WfMS) we call SecFlow in answer to the recent developments of combining workflow management systems with Cloud environments and the still lacking abilities of such systems to ensure the security and privacy of cloud-based workflows. The SecFlow architecture focuses on full workflow life cycle coverage as, in addition to the existing approaches to design security-aware processes, there is a need to fill in the gap of maintaining security properties of workflows during their execution phase. To address this gap, we derive the requirements for such a security-aware WfMS and design a system architecture that meets these requirements. SecFlow integrates key functional components such as secure model construction, security-aware service selection, security violation detection, and adaptive response mechanisms while considering all potential malicious parties in multi-tenant and cloud-based WfMS.Comment: 16 pages, 6 figure

Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent's private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.Comment: 11 pages, 2 figure

Using Confidential Data for Domain Adaptation of Neural Machine Translation

We propose a multilingual method for the extraction of biased sentences from Wikipedia, and use it to create corpora in Bulgarian, French and English. Sifting through the revision history of the articles that at some point had been considered biased and later corrected, we retrieve the last tagged and the first untagged revisions as the before/after snapshots of what was deemed a violation of Wikipedia's neutral point of view policy. We extract the sentences that were removed or rewritten in that edit. The approach yields sufficient data even in the case of relatively small Wikipedias, such as the Bulgarian one, where 62k articles produced 5k biased sentences. We evaluate our method by manually annotating 520 sentences for Bulgarian and French, and 744 for English. We assess the level of noise and analyze its sources. Finally, we exploit the data with well-known classification methods to detect biased sentences. Code and datasets are hosted at https://github.com/crim-ca/wiki-bias

Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent's private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.Comment: 12 pages, 4 figure

KotlinDetector:Towards Understanding the Implications of Using Kotlin in Android Applications

Java programming language has been long used to develop native Android mobile applications. In the last few years many companies and freelancers have switched into using Kotlin partially or entirely. As such, many projects are released as binaries and employ a mix of Java and Kotlin language constructs. Yet, the true security and privacy implications of this shift have not been thoroughly studied. In this work, a state-of-the-art tool, KotlinDetector, is developed to directly extract any Kotlin presence, percentages, and numerous language features from Android Application Packages (APKs) by performing heuristic pattern scanning and invocation tracing. Our evaluation study shows that the tool is considerably efficient and accurate. We further provide a use case in which the output of the KotlinDetector is combined with the output of an existing vulnerability scanner tool called AndroBugs to infer any security and/or privacy implications