Mobile app users' privacy concerns: different heuristics for privacy assurance statements in the EU and China

PurposeAs mobile apps request permissions from users, protecting mobile users' personal information from being unnecessarily collected and misused becomes critical. Privacy regulations, such as General Data Protection Regulation in the European Union (EU), aim to protect users' online information privacy. However, one’s understanding of whether these regulations effectively make mobile users less concerned about their privacy is still limited. This work aims to study mobile users' privacy concerns towards mobile apps by examining the effects of general and specific privacy assurance statements in China and the EU.Design/methodology/approachDrawing on ecological rationality and heuristics theory, an online experiment and a follow-up validation experiment were conducted in the EU and China to examine the effects of privacy assurance statements on mobile users' privacy concerns.FindingsWhen privacy regulation is presented, the privacy concerns of Chinese mobile users are significantly lowered compared with EU mobile users. This indicates that individuals in the two regions react differently to privacy assurances. However, when a general regulation statement is used, no effect is observed. EU and Chinese respondents remain unaffected by general assurance statements.Originality/valueThis study incorporates notions from fast and frugal heuristics end ecological rationality – where seemingly irrational decisions may make sense in different societal contexts.

Why Does Privacy Paradox Exist?

In this exploratory study, the main aim was to find, ‘why do people disclose information when they are concerned about their privacy?’. The reasons that provide a plausible explanation to the privacy paradox have been conjectural. From the analysis of the eighteen in-depth interviews using grounded theory, themes were then conceptualized. We found rational and irrational explanations in terms of cognitive biases and heuristics that explain the privacy paradox among mobile users. We figured out some reasons in this context of mobile computing which were not emphasized earlier in the privacy paradox literature such as Peanut Effect, Fear of Missing Out- FoMo, Learned Helplessness, and Neophiliac Personality. These results add to the privacy paradox discourse and provide implications for smartphone users for making privacy-related decisions more consciously rather than inconsiderately disclosing information. Also, the results would help marketers and policymakers design nudges and choice architectures that consider privacy decision-making hurdles.

E-Commerce and the Factors Affecting Its Development in the Age of Digital Technology: Empirical Evidence at EU–27 Level

The purpose of this study is to identify the factors influencing e-commerce and to evaluate the long and short-term impacts on the development of e-commerce activity. After establishing the hypotheses to verify, we use multiple panel regressions to test the influence of education level, consumer’s residence, consumer’s labour market status, internet banking, mobile and non-mobile users on the development of e-commerce. For this matter, in this paper, by adopting the fully modified ordinary least squares (FMOLS) method and a vector error correction model (VECM), we performed an empirical analysis of the nexus between education level, consumer’s residence, consumer’s labour market status, internet banking and mobile and non-mobile users and e-commerce, based on panel data for EU–27 countries from 2011 to 2020. The results of the study indicate that all the variables involved in the two econometric models and associated with education level, consumer’s residence, labour market status, internet banking, mobile and non-mobile users, all have significant impacts on the development of e-commerce. Most of the variables positively influence the development of e-commerce except for internet purchases by individuals un-employed and for internet purchases by non-mobile users that are negatively correlated with e-commerce activity.

ES-HAS: ECC-Based Secure Handover Authentication Scheme for Roaming Mobile User in Global Mobility Networks

The design and implementation of two-factor schemes designed for roaming mobile users for global mobility networks in smart cities requires attention to protect the scheme from various security attacks, such as the replay attack, impersonation attack, man-in-the-middle attack, password-guessing attack and stolen-smart-card attack. In addition to these attacks, the scheme should achieve user anonymity, unlinkability and perfect forward secrecy. In the roaming scenario, as mobile users are connected to the foreign network, mobile users must provide authentication details to the foreign network to which they are connected. The foreign network forwards the authentication messages received from the mobile users to their home network. The home network validates the authenticity of the mobile user. In the roaming scenario, all communication between the three entities is carried over an insecure channel. It is assumed that the adversary has the capabilities to intercept the messages transmitted over an insecure channel. Hence, the authentication scheme designed must be able to resist the above-mentioned security attacks and achieve the security goals. Our proposed scheme ES-HAS (elliptic curve-based secure handover authentication scheme) is a two-factor authentication scheme in which the mobile user possesses the password, and the smart card resists the above-mentioned security attacks. It also achieves the above-mentioned security goals. We also extended our two-factor authentication to a multi-factor authentication scheme using the fingerprint biometric technique. The formal security analysis using BAN logic and the formal security verification of the proposed scheme using the widely accepted AVISPA (automated validation of internet security protocols and applications) tool is presented in this article. In comparison with the related schemes, the proposed scheme is more efficient and robust. This makes the proposed scheme suitable for practical implementation.

LB-DDQN for Handover Decision in Satellite-Terrestrial Integrated Networks

The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.

On Location Privacy Protection Algorithm Based on Edge Computing and Caching

To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.

Latency-aware secure transmission in MEC-assisted HSR communication systems

Mobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method.