Protecting Privacy on Mobile Apps: A Principal–Agent Perspective

Although individuals increasingly use mobile applications (apps) in their daily lives, uncertainty exists regarding how the apps will use the information they request, and it is necessary to protect users from privacy-invasive apps. Recent literature has begun to pay much attention to the privacy issue in the context of mobile apps. However, little attention has been given to designing the permission request interface to reduce individuals’ perceived uncertainty and to support their informed decisions. Drawing on the principal–agent perspective, our study aims to understand the effects of permission justification, certification, and permission relevance on users’ perceived uncertainty, which in turn influences their permission authorization. Two studies were conducted with vignettes. Our results show that certification and permission relevance indeed reduce users’ perceived uncertainty. Moreover, permission relevance moderates the relationship between permission justification and perceived uncertainty. Implications for theory and practice are discussed.

Gamification on Chatbot-Based Learning Media: a Review and Challenges

The mobile learning sector has exploded, implying that the e-Learning trend is shifting to mobile platforms. As a result, chatbots have become increasingly popular alternatives for online learning and examinations on mobile platforms. However, it did not provide enough motivation for the student. On the other hand, gamification in a typical e-Learning platform is a widely used technique for increasing students' learning motivation. Therefore, combining gamification with chatbot-based learning and examinations possibly offer benefits, including increase student learning motivation. This study explored the possibilities and future challenges of the development of gamification within chatbot-based learning media. We discussed four aspects: architecture's reliability, security and privacy issue, user’s acceptance and motivation, and gamification feature challenges.

The Students’ Perspective of Using Instagram as a Writing Assignment Platform

This study aimed to find out the students’ perspective of using Instagram as a writing assignment platform. Using a qualitative research method, this study involved the students of the 2nd semester in English Department Universitas Islam Syekh-Yusuf Tangerang. There were 61 students in writing class who were involved as the participants. It used the technique of survey with open-ended questionnaire as the instrument of data collection The results show that almost all students have positive perspective towards Instagram as a writing assignment platform. The participants found out that Instagram was fun, easy, and effective media to be used in doing writing assignment. Although, there were also some barriers in using it such as, bad internet network and privacy issue. It can be concluded that, Instagram can be one of useful teaching media, especially for writing, as long as it is used and supervised appropriately.

Differentially Private Pairwise Learning Revisited

Instead of learning with pointwise loss functions, learning with pairwise loss functions (pairwise learning) has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. However, most of the existing algorithms for pairwise learning fail to take into consideration the privacy issue in their design. To address this issue, previous work studied pairwise learning in the Differential Privacy (DP) model. However, their utilities (population errors) are far from optimal. To address the sub-optimal utility issue, in this paper, we proposed new pure or approximate DP algorithms for pairwise learning. Specifically, under the assumption that the loss functions are Lipschitz, our algorithms could achieve the optimal expected population risk for both strongly convex and general convex cases. We also conduct extensive experiments on real-world datasets to evaluate the proposed algorithms, experimental results support our theoretical analysis and show the priority of our algorithms.

Influence of Privacy Fatigue of Social Media Users on Their Privacy Protection Disengagement Behaviour—A PSM based Analysis

This paper aims to examine the net effect of privacy fatigue of social media users on privacy protection disengagement behaviour, which is helpful to address the users’ privacy issue in the new stage of social media digitalization. Applying the Propensity Score Matching(PSM) methodology, the authors conduct the data analysis of 1,734 samples of social media users and eliminates the selectivity error caused by individual characteristic variables so as to improve the prediction accuracy of variable causality. Their research not only validates the causal relationship between privacy fatigue and privacy protection disengagement, proving that privacy fatigue can directly lead to privacy protection disengagement behaviour but also reveals that the individual characteristic variables have heterogeneous effects on the influence of privacy fatigue on protection disengagement behaviour.

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Over the last decade, blockchain has been considered an encouraging solution to secure distributed ledgers. Moreover, with the introduction of a pseudonymous payment method without a centralized database or authoritative person, blockchain has also evolved as the future generation for online payment system. However, with the eruption of a large scale database, scalability has also become a demanding issue. In addition to the obstacle mentioned above, challenges like security and scalability stop accelerated adjustments for the development of smart cities. Without directing this essential scalability and privacy issue, such an encouraging method may not help develop smart cities. This paper bestows a measure to analyze both scalability and security aspects of existing blockchain methods with applications of smart city networks. The proposed method is known as Gradient Smart Load Balancer and Blockchain Dempster Shafer Reputation (GSLB-BDSR). Gradient Smart Load Balancer is designed so that even though with the increase in the number of participating sensors, the load is said to balance by applying gradient function, therefore ensuring scalability. Next, to cover the security aspect, with the aid of scalable blocks in the blockchain network, a Blockchain Dempster Shafer Reputation model is proposed. Evaluation outcomes of proposed security solutions outperform conventional solutions.

Privacy and Trust Redefined in Federated Machine Learning

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.

Optimized Authentication System with High Security and Privacy

Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.