Message Rate Research Articles

Are continuous stop-and-go mixnets provably secure?

This work formally analyzes the anonymity guarantees of continuous stop-and-go mixnets and attempts to answer the titular question. Existing mixnet based anonymous communication protocols that aim to provide provable anonymity guarantees rely on round-based communication models, which requires synchronization among all the nodes and clients that is difficult to achieve in practice. Continuous stop-and-go mixnets (e.g., Loopix and Nym) provide a nice alternative by adding a random delay for each message on every hop independent of all other hops and all other messages. The core anonymization technique of continuous mixnets combined with the fact that the messages are sent by the clients to the mixnet at different times makes it a difficult problem to formally prove security for such mixnet protocols; existing end-to-end analyses for such designs provide only experimental evaluations for anonymity and were lacking a comprehensive formal treatment. We are the first to close that gap and provide a formal analysis. We provide two indistinguishability based definitions (of sender anonymity), namely pairwise unlinkability and user unlinkability, tuned specifically for continuous stop-and-go mixnets. We derive the adversarial advantage as a function of the protocol parameters for the two definitions. We show that there is a fundamental lower bound on the adversarial advantage $\delta$ for pairwise unlinkability; however, strong user unlinkability (negligible adversarial advantage) can be achieved if the users message rate ($\lambda_u$) is proportional to message processing rate ($\lambda$) on the nodes.

Insight into the Trends in Research on the Impact of Social Media on Adolescent Mental Health

This study investigates the impact of social media on adolescent mental health through a bibliometric analysis of literature from 2004 to 2024 in the web of science database. With internet usage among adolescents exceeding 87% and instant messaging rates reaching 84.1%, platforms such as Facebook and Instagram significantly affect their mental well-being. The study identifies “anxiety,” “depression,” and “adolescents” as central themes, demonstrating a strong correlation between high-frequency social media use and mental health issues. Adolescents, the most affected group, face significant risks from prolonged social media exposure. The United Kingdom leads study collaborations in this field. The findings highlight the complexity of social media’s impact on mental health, influenced by various factors ranging from childhood experiences to specific psychological issues. Emphasizing adolescent mental health, the study calls for future study to explore the long-term effects of social media use and develop effective intervention measures and policy recommendations. This scientific evidence aims to guide policymakers, educators, and parents in promoting the comprehensive development and well-being of adolescents, helping them navigate the digital age healthily and safely.

Tissue Retrieval of Laparoscopically Excised Adnexal Specimens in Gynecologic Surgery: Posterior Culdotomy versus Abdominal Extraction

ObjectiveThe objective of this study is to compare intraoperative and postoperative outcomes in women undergoing removal of adnexal structures by either posterior culdotomy or abdominal extraction. DesignThis is a retrospective cohort study conducted via medical record review. Demographic, clinical, and operative variables were abstracted from the medical records. Statistical analysis consisted of descriptive statistics, Fisher's exact tests, Wilcoxon rank sum tests, and multivariable logistic regression models. SettingSingle academic tertiary care center between 2010 and 2022. ParticipantsA total of 718 patients were identified and included in our analysis who underwent minimally invasive ovarian cystectomy or oophorectomy. Patients were excluded if they underwent concomitant hysterectomy. InterventionsPatients underwent minimally invasive oophorectomy or ovarian cystectomy, and specimens were extracted by either abdominal extraction (AE) or culdotomy extraction (CE). Measurements and Main ResultsOf the 718 patients who met inclusion criteria, 127 (17.7%) underwent CE, and 591 (82.3%) underwent abdominal extraction. The CE group had longer operative times (113 minutes vs 96 minutes, p <.001) and higher estimated blood loss (25 mL vs 10 mL, p <.001) compared to the abdominal extraction group. There were more malignancies in the CE than the abdominal extraction group (15.7% vs 8.1%, respectively, p <.001). After adjusting for potential confounders, those who underwent CE were more likely to have 2 or more clinic visits (OR 2.89; 95% confidence interval, 1.66–5.03; p <.001) and call or message the clinic (OR 2.08; 95% confidence interval, 1.35–3.20; p <.001). There were no incidences of cuff dehiscence, cuff cellulitis, or pelvic abscess in either group. ConclusionRemoval of adnexal specimens via abdominal port site or posterior culdotomy incision is a feasible option for specimen extraction and can be individualized based on patient and surgeon preference and patient factors. Those undergoing CE may require more preoperative counseling due to higher rate of postoperative messages seen in our cohort.

Polar code-based secure transmission with higher message rate combining channel entropy and computational entropy

The existing physical layer security schemes, which are based on the key generation model and the wire-tap channel model, achieve security by utilizing channel reciprocity entropy and noise entropy, respectively. In contrast, we propose a novel secure transmission framework that combines noise entropy with reciprocity entropy, achieved by inserting reciprocity entropy into the frozen bits of polar codes. Note that in real-world scenarios, when eavesdroppers employ polynomial-time attacks, the bit error rate (BER) increases due to the introduction of computational entropy. To achieve indistinguishability security, we convert the practical physical layer security metric, BER, into the average min-entropy, a widely accepted concept in cryptography. The simulation results demonstrate that the eavesdropper’s BER can be significantly increased without compromising the communication performance of the legitimate receiver. Under concrete parameters we selected, when compared to the joint scheme of physical layer key generation and one time pad, the modular semantically-secure scheme based on the wire-tap channel model, and the simple channel entropy combination scheme, our scheme achieves a message rate approximately 1.2 times, 3.8 times, and 1.4 times better, respectively. Experimental testing validates the feasibility of our scheme.

Social Media in Crisis Communication: A Case Analysis of the 2023 Kahramanmaraş Earthquakes

This study examines the role of Twitter/X, a news platform, in crisis communication. The aim of the study is to contribute to the literature on crisis communication by understanding the importance, effectiveness, and applicability of social media. For this purpose, a qualitative research was conducted to determine how X was used as a communication tool during crises, specifically focusing on a case analysis of the Kahramanmaraş earthquakes. Within the scope of the research, 182 tweets shared between February 6th and 13th, 2023, from the official X account of the Disaster and Emergency Management Presidency (AFAD), were examined. Content analysis method was employed to analyze the tweets, focusing on content, frequency of sharing, and interaction rate. The research findings indicate that AFAD's tweets were categorized into four main themes: "information on processes, support services, announcements, and disinformation and communication." The findings suggest that the posts were predominantly made during the initial period of the crisis characterized by high uncertainty, with the most shared content focusing on search and rescue operations. Another research objective was to determine the interaction rate of AFAD's crisis communication messages. Accordingly, it is noteworthy that the theme with the least frequency of sharing, "disinformation and communication," received the highest interaction from users. The findings of this study, which focuses on the transformative power of social media in crisis communication, confirm that the need for reliable communication channels and verified information/sources increases during crises, and it confirms the significant role of institutional social media accounts in combating disinformation.

New Frontiers in Health Literacy: Using ChatGPT to Simplify Health Information for People in the Community

BackgroundMost health information does not meet the health literacy needs of our communities. Writing health information in plain language is time-consuming but the release of tools like ChatGPT may make it easier to produce reliable plain language health information.ObjectiveTo investigate the capacity for ChatGPT to produce plain language versions of health texts.DesignObservational study of 26 health texts from reputable websites.MethodsChatGPT was prompted to ‘rewrite the text for people with low literacy’. Researchers captured three revised versions of each original text.Main MeasuresObjective health literacy assessment, including Simple Measure of Gobbledygook (SMOG), proportion of the text that contains complex language (%), number of instances of passive voice and subjective ratings of key messages retained (%).Key ResultsOn average, original texts were written at grade 12.8 (SD = 2.2) and revised to grade 11.0 (SD = 1.2), p < 0.001. Original texts were on average 22.8% complex (SD = 7.5%) compared to 14.4% (SD = 5.6%) in revised texts, p < 0.001. Original texts had on average 4.7 instances (SD = 3.2) of passive text compared to 1.7 (SD = 1.2) in revised texts, p < 0.001. On average 80% of key messages were retained (SD = 15.0). The more complex original texts showed more improvements than less complex original texts. For example, when original texts were ≥ grade 13, revised versions improved by an average 3.3 grades (SD = 2.2), p < 0.001. Simpler original texts (< grade 11) improved by an average 0.5 grades (SD = 1.4), p < 0.001.ConclusionsThis study used multiple objective assessments of health literacy to demonstrate that ChatGPT can simplify health information while retaining most key messages. However, the revised texts typically did not meet health literacy targets for grade reading score, and improvements were marginal for texts that were already relatively simple.

PDTM: A Provenance-Driven dynamic Trust Management model for IoVs

Internet of Vehicles (IoVs) play an essential role in traffic safety and travel efficiency. However, there is still risk where malicious vehicles without validation or evaluation, to tamper with messages and pollute network with compromised information. Upon fruitful computing resource in 6G, this paper proposes a Provenance-Driven dynamic Trust Management (PDTM) model for IoVs. PDTM integrates data provenance into the trust evaluation of vehicles and messages. Different from literature that only evaluates trust through vehicle, the high reliability of Road Side Unit (RSU) is concerned herein. Specifically, the recommendation trust is calculated by RSU, to eliminate false recommendations of malicious vehicles. Considering the dynamic and real-time nature of trust, this paper proposes a global-based threshold update mechanism. The mechanism can dynamically adjust the threshold, according to the trust value of vehicles. Simulation experiments verify the validity of PDTM in the detection rate of malicious vehicles and delivery rate of authentic messages.

Feasibility and Usability of a Mobile App-Based Interactive Care Plan for Migraine in a Community Neurology Practice: Development and Pilot Implementation Study.

Migraine is a common and major cause of disability, poor quality of life, and high health care use. Access to evidence-based migraine care is limited and projected to worsen. Novel mobile health app-based tools may effectively deliver migraine patient education to support self-management, facilitate remote monitoring and treatment, and improve access to care. The risk that such an intervention may increase the care team workload is a potential implementation barrier. This study aims to describe a novel electronic health record-integrated mobile app-based Migraine Interactive Care Plan (MICP) and evaluate its feasibility, usability, and impact on care teams in a community neurology practice. Consecutive enrollees between September 1, 2020, and February 16, 2022, were assessed in a single-arm observational study of usability, defined by 74.3% (127/171) completing ≥1 assigned task. Task response rates, rate and type of care team escalations, and patient-reported outcomes were summarized. Patients were prospectively recruited and randomly assigned to routine care with or without the MICP from September 1, 2020, to September 1, 2021. Feasibility was defined by equal to or fewer downstream face-to-face visits, telephone contacts, and electronic messages in the MICP cohort. The Wilcoxon rank-sum test was used to compare continuous variables, and the chi-square test was used for categorical variables for those with at least 3 months of follow-up. A total of 171 patients were enrolled, and of these, 127 (74.3%) patients completed ≥1 MICP-assigned task. Mean escalations per patient per month was 0.9 (SD 0.37; range 0-1.7). Patient-confirmed understanding of the educational materials ranged from 26.6% (45/169) to 56.2% (95/169). Initial mean headache days per week was 4.54 (SD 2.06) days and declined to 2.86 (SD 1.87) days at week 26. The percentage of patients reporting favorable satisfaction increased from a baseline of 35% (20/57) to 83% (15/18; response rate of 42/136, 30.9% to 28/68, 41%) over the first 6 months. A total of 121 patients with MICP were compared with 62 patients in the control group. No differences were observed in the rate of telephone contacts or electronic messages. Fewer face-to-face visits were observed in the MICP cohort (13/121, 10.7%) compared with controls (26/62, 42%; P<.001). We describe the successful implementation of an electronic health record-integrated mobile app-based care plan for migraine in a community neurology practice. We observed fewer downstream face-to-face visits without increasing telephone calls, medication refills, or electronic messages. Our findings suggest that the MICP has the potential to improve patient access without increasing care team workload and the need for patient input from diverse populations to improve and sustain patient engagement. Additional studies are needed to assess its impact in primary care.

Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET

Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load.

Misinformation Is Contagious: Middle school students learn how to evaluate and share information responsibly through a digital game

Digital games have emerged as promising tools for countering the spread of misinformation online. Previous studies have mostly used games to inoculate players against misleading communication techniques. There has also been a lack of research on misinformation games in middle school. Hence, the aims of this investigation were to examine to what extent a game can support middle school students' competence to evaluate online information and their dispositions to share information responsibly. For this purpose, we developed a game, Misinformation Is Contagious, that models reliable evaluation strategies and the social implications of sharing (in)accurate information. In two studies with 7th and 8th grade students (N = 84 and N = 131), we found that playing the misinformation game resulted in better accuracy discernment, sharing discernment, and metastrategic knowledge about corroboration, compared to playing a control language game. In Study 1, the effects on discernment scores were mainly due to higher ratings of accurate messages; whereas in Study 2, the effects were mainly due to lower ratings of inaccurate messages. In both studies, accuracy discernment mediated the effect of playing the misinformation game on sharing discernment. In Study 2, the misinformation game also had a direct effect on sharing discernment, suggesting it may have impacted players' dispositions to value accuracy while sharing. However, the game did not affect students' self-reported stances regarding sharing misinformation. These results provide initial evidence that a game designed to support evaluation strategies can help students resist misinformation and identify reliable information. The findings also suggest that games can potentially promote responsible information sharing.

Energy Efficiency Maximization in RIS-Assisted SWIPT Networks With RSMA: A PPO-Based Approach

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

Anticipating adaptation: tracking the impact of planned and unplanned adaptations during the implementation of a complex population-based genomic screening program.

In 2021, the Medical University of South Carolina (MUSC) launched In Our DNA SC. This large-scale initiative will screen 100,000 individuals in South Carolina for three preventable hereditary conditions that impact approximately two million people in the USA but often go undetected. In anticipation of inevitable changes to the delivery of this complex initiative, we developed an approach to track and assess the impact of evaluate adaptations made during the pilot phase of program implementation. We used a modified version of the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) and Adaptations to code adaptations made during the 3-month pilot phase of In Our DNA SC. Adaptations were documented in real-time using a REDCap database. We used segmented linear regression models to independently test three hypotheses about the impact of adaptations on program reach (rate of enrollment in the program, rate of messages viewed) and implementation (rate of samples collected) 7 days pre- and post-adaptation. Effectiveness was assessed using qualitative observations. Ten adaptations occurred during the pilot phase of program implementation. Most adaptations (60%) were designed to increase the number and type of patient contacted (reach). Adaptations were primarily made based on knowledge and experience (40%) or from quality improvement data (30%). Of the three adaptations designed to increase reach, shortening the recruitment message potential patients received significantly increased the average rate of invitations viewed by 7.3% (p = 0.0106). There was no effect of adaptations on implementation (number of DNA samples collected). Qualitative findings support improvement in effectiveness of the intervention after shortening the consent form and short-term positive impact on uptake of the intervention as measured by team member's participation. Our approach to tracking adaptations of In Our DNA SC allowed our team to quantify the utility of modifications, make decisions about pursuing the adaptation, and understand consequences of the change. Streamlining tools for tracking and responding to adaptations can help monitor the incremental impact of interventions to support continued learning and problem solving for complex interventions being delivered in health systems based on real-time data.

Efficient Congestion Control With Information Loss Minimization for Vehicular Ad Hoc Networks

The reduction of beacon message rates is of great significance to network congestion control in Vehicular Ad Hoc Networks (VANETs). However, this may cause large traffic information loss and serious traffic accidents. In this paper, we propose an efficient congestion control protocol, which not only alleviates channel congestion but also minimizes information loss by adaptively adjusting the beacon rates. Unlike conventional methods, this protocol investigates beacon transmission from a perspective of information loss, and adopts different beacon rates for different vehicles. First, we design an information loss factor to quantify the information deviation between the original information generated by a vehicle and the actual information received by its neighbor vehicles. Then, based on the information loss factor, we transform the beacon rate adjustment problem into an information loss minimization problem, which is addressed by a greedy beacon-rate adjustment algorithm. In addition, based on the derived different beacon rates, we design a periodicity-based time-slot selection scheme to allocate idle time slots to different vehicles, in order to alleviate transmission collisions. Extensive simulation results show the advantages of our solution in terms of multiple metrics.

Vehicular Networks Dynamic Grouping and Re-Orchestration Scenarios

The topological structure in vehicular communication networks presents challenges for sustaining network connectivity on the road. Highway dynamics, for example, encourage the need for an adaptive and flexible structure to handle the rapid events of vehicles joining and leaving the road. Such demand aligns with the advancement made in software-defined networks and related dynamic network re-orchestration. This paper discusses the development of a virtual model that represents the operation of an autonomous vehicular network. It also investigates the ability to re-orchestrate the topology through software definition while running the various operational phases. Network self-formation, network expansion, retraction via vehicular members joining and leaving, and network self-healing when a topological rupture occurs as a result of a key member leaving the network are the key grouping phases. The communication approach is analyzed based on the status of network members and their ability to assume the various network roles. The concept is tested using both a Contiki–Cooja network simulator and a MATLAB analytical modeling tool to reflect the operation and performance of the grouping approach under various road scenarios. The outcome of the analysis reflects the ability of the group to be formulated within a measured latency considering the various network parameters such as communication message rate. The approach offers tools for managing the dynamic connectivity of vehicular groups and may also be extended to assume the function of an on-road network digital twin during the lifetime of a given group.

Multi-Round Message Scheduling for Fast GNSS Packet Broadcasting

Global navigation satellite systems (GNSSs) may transmit data to provide additional services. Since the resources reserved for these data are typically fixed and the message rate is low, we propose to split long messages into shorter packets and properly schedule their transmission from the entire constellation of satellites. Considering an efficient data transmission (useful for example for search and rescue messages), we aim at the scheduling of packets on the satellites on multiple rounds with two objectives: either a) the minimization of the maximum latency among all receivers, or b) the maximization of the average received packets per round. We first derive bounds on the performance of any GNSS single or multi-round scheduling solution, on which the proposed scheduling solutions are based. Then we introduce the scheduling problems that turn out to be integer linear programming (ILP) problems. Lastly, we assess their performance, showing that our solution minimizes the maximum latency, while the scheduling targeting the average latency outperforms existing literature solutions.

Message self and social relevance increases intentions to share content: Correlational and causal evidence from six studies.

Information sharing within social networks can catalyze widespread attitudinal and behavioral change and the chance to share information with others has been characterized as inherently valuable to people. But what are the sources of value and how might they be leveraged to promote sharing? We test ideas from the value-based virality model that the value of sharing increases when people perceive messages as more relevant to themselves and to people they know, resulting in stronger intentions to share. We extend this work by considering how sharing context-broadcasting to a wide audience or narrowcasting directly to someone-may alter these relationships. Six online studies with adults in the United States (N participants = 3,727; messages = 362; message ratings = 30,954) showed robust evidence that self and social relevance are positively and uniquely related to sharing intentions within- and between-person. Specification curve analysis showed these relationships were consistent across message content (COVID-19, voting, general health, climate change), medium (social media post and news articles), and sharing context (broad- and narrowcasting). A preregistered experiment showed that manipulating the self and social relevance of messages through a framing manipulation causally increased sharing intentions. These causal effects were mediated by changes in both self and social relevance, but the relative strength of the causal pathways differed depending on sharing context. These findings extend existing models of information sharing, and highlight self and social relevance as psychological mechanisms that motivate information sharing that can be targeted to promote sharing across contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Effective Security Scheme for Attacks on Sample Value Messages in IEC 61850 Automated Substations

The trend of transforming substations into smart automated facilities has led to their swift digitalization and automation. To facilitate data exchange among equipment within these substations, the IEC 61850 standard has become the predominant standard. However, this standardization has inadvertently made these substations more susceptible to cyberattacks, which is a significant concern given the confidential information that is transmitted. As a result, cybersecurity in substations is becoming an increasingly critical topic. IEC 62351 standard provides guidelines and considerations for securing the IEC 61850 messages to mitigate their vulnerabilities. While securing Generic Object-Oriented Substation Event (GOOSE) messages has received considerable attention in literature, the same level of scrutiny has not been applied to Sampled Value (SV) messages despite their susceptibility to cyberattacks and similar frame format. This paper presents the impact of replay and masquerade attacks on SV messages. It also develops a scheme for securing SV messages against these attacks. Due to high sampling rate and time critical nature of SV messages, the time complexity of security scheme is critical for its applicability to SV messages. Hence, in this work, SV emulators have been developed in order to send these modified secure SV messages and investigate their timing performance. The results show that the proposed scheme can mitigate replay and masquerade attacks on SV messages while providing the necessary high sampling rate and stringent timing requirements.

A Certificateless Anonymous Cross-Domain Authentication Scheme Assisted by Blockchain for Internet of Vehicles

With the development of the Internet of Things and the increase of intelligent vehicles, the Internet of Vehicles (IoVs) have been widely used in the information communication such as road and traffic conditions. However, heavy overhead of certificate management, high computing load of identity and message authentication, and the privacy disclosure of vehicle nodes have hindered the development of intelligent transportation. In this study, we propose a certificateless cross-domain anonymous authentication scheme based on blockchain for IoVs. Specifically, the vehicle identity information is authenticated by the first roadside unit (RSU), and transactions are recorded permanently and immutably in the blockchain to reduce the repeated authentication load of other RSUs. To achieve conditional privacy, the trusted authority (TA) generates pseudonyms for each registered user. The relation between the pseudonym and the real identity is kept confidential by the TA and only can only be revealed in case of disputes. Meanwhile, the private key of the vehicle is generated anonymously on the basis of certificateless technology and the pairing-free signature verification. Correctness and security proof demonstrate that our proposed scheme is provably secure and can withstand different types of attacks. A simulation environment has been built to test the packet loss rate and delay of messages in the network. Results show that the proposed scheme is more efficient than the related schemes.

Secret Key-Enabled Authenticated-Capacity Region, Part—II: Typical-Authentication

This paper investigates the secret key-authenticated-capacity region, where information-theoretic authentication is defined by the ability of the decoder to accept and decode messages originating from a valid encoder while rejecting messages from other invalid sources. The model considered here consists of a valid encoder-decoder pairing that can communicate through a channel controlled by an adversary who is also able to eavesdrop on the encoder’s transmissions. Prior to the encoder’s transmission, the adversary decides whether or not to replace the decoder’s observation with an arbitrary one of the adversary’s choosing, with the adversary’s objective being to have the decoder accept and decode their observation to a valid message (different from that of the encoder). To combat the adversary, the encoder and decoder share a secret key. The secret key-authenticated-capacity region is defined as the region of jointly achievable message rate, authentication rate (a to be defined per symbol measure that will generally represent the likelihood that an adversary can fool the decoder), and the key-consumption rate (how many bits of secret key are needed per symbol sent). This is the second of a two-part study, with the parts differing in their measure of the authentication rate. For this second study, the probability of false authentication is considered as a function of the system state, where the system state is defined by the message being transmitted, the value of the secret key, the adversary’s channel observations, and the adversary’s (possibly stochastic) choice for the decoder’s observation. Termed the typical-authentication rate, the authentication measure considered here corresponds to an upper bound on the probability of false authentication for the majority of system states. For this measure, we derive matching inner and outer bounds for the secret key-enabled authenticated capacity region in terms of traditional information-theoretic measures. In doing so, it is shown that the typical-authentication rate and the message rate exhibit a one-to-one trade-off in the capacity region.

CONNECTION TO THE USE OF FREE ANDROID APPS IN KEBBI STATE, NIGERIA

In this paper, we studied scamming emanating from the use of free Android apps by conducting an online survey with respondents recruited from four tertiary institutions in Kebbi State, Nigeria. We found that the apps used for communication, such as Facebook and WhatsApp, have the highest number of users. Also, 55.26 % of users reported receiving scam messages that could be linked to their use of free apps, whereas 32.45 % of them were indeed defrauded by scammers. The study concludes that the free social media apps coupled with the characteristic of the users’ login credentials could be responsible for the high rate of scam messages that the respondents receive regularly.