Privacy Settings Research Articles

Dead Users’ Profiles on Facebook: Limited Interaction Beyond Human Existence

Abstract Death has become increasingly visible on social networks, especially after COVID-19, and Facebook addresses that with double standards: while some profiles remain active, others turn into memorials. This article investigates how Facebook’s system deals with dead users’ profiles either to support or restrict interactions concerning users’ deaths. Our qualitative analysis of data from 54 public profiles of people who died between June 2020 and March 2021 showed that (i) Facebook fails to communicate the criteria for transforming profiles into memorials; (ii) no information about their contacts of deceased users’ profiles is given; (iii) the frequency of interaction with memorials and with active profiles is different; (iv) profiles’ privacy settings shape interaction. Our results exemplify how a sociotechnical system influences people’s interactions with dead users’ profiles. We herein highlight implications for interaction design and evaluation, besides the need to consider interaction as existence, which raises big challenges to the Human Computer Interaction (HCI) community.

Understanding information disclosures and privacy sensitivity on short-form video platforms: An empirical investigation

As a new social media platform, short-form video apps integrate the dual functions of e-commerce and social networking. The proliferation and high penetration rate of this kind of app have resulted in online user concerns about the privacy security problems exposed by such platforms. This study focuses on better understanding the impact mechanism of private information disclosure behaviors and obtained benefits on privacy concerns of short-form video platform users. We analyzed the behavioral and privacy setting data of 35,456 users registered on the TikTok platform. The results demonstrated a correlation between use behaviors (e.g., participation and interest) and privacy sensitivity, among which popularity, as a form of external feedback, played a mediating role. In addition, the official certification provided to users by the platform moderated the impact of user popularity on privacy sensitivity. This study enriches the understanding of the correlation between privacy concerns and information disclosure behavior and suggests practical implications for the design of privacy settings and protections by short-form video platforms.

Multi-party Privacy Set Intersection protocol with an Untrusted Cloud Server

The development of cloud computing has brought many conveniences to life, its advantages of low cost and high computing power bring convenience to our life, but the main problem is how to protect user data on untrustworthy cloud servers. To solve this problem, we have proposed a new private set intersection protocol, denoted as. The protocol uses a representation of sets with polynomial point-value pairs. All elements should be transmitted and computed in the protocol as ciphertext by introducing random polynomials to re-randomize the encryption elements. To reduce the computational burden of each party. The parties send the processed polynomials to the cloud server, which calculates the intersection. In addition, our protocol achieves fairness in the case of collusion between parties. Finally, the security of the protocol is demonstrated under the UC model. Comparing the performance of this protocol with other related protocols show that the protocol is better than the other associated protocols in terms of low computational overhead.

High-throughput and area-efficient architectures for image encryption using PRINCE cipher

Internet of Things (IoT) has gained popularity in recent years and has engulfed nearly every industry. The widespread use of numerous ubiquitous computing devices in the low-resource domain has resulted in a new set of privacy and security concerns. To address the problem of security in resource-constrained devices, many lightweight algorithms have been developed. This paper proposes optimized hardware implementations of the lightweight PRINCE block cipher, with the aim of providing adequate security while maximizing resource efficiency. The proposed architecture uses fewer resources and provides a reasonable trade-off between area footprint and efficiency. In the proposed unrolled pipelined architecture, the encryption round is divided into three sub-stages, with registers inserted in between. Using this design approach, the operating frequency is greatly improved. As a result, this architecture adapts itself effectively to high-performance applications. This paper also proposes serial-based and round-based architectures for resource-constrained devices. The proposed unrolled sub pipeline PRINCE block cipher is implemented on the Virtex-6-FF784 and Virtex-4-FF668 FPGA device families and achieved substantial improvements in throughput of 13.057% and 113%, respectively, as well as efficiency of 8.109% and 113.734% respectively. The proposed architecture is evaluated on a variety of grayscale images, and the security analysis is performed using MATLAB software. Aside from that, the proposed architecture uses the CBC-mode of operation. The security analysis and encryption outputs show that the proposed architecture is an effective choice for image encryption and provides sufficient security to the cipher images.

Understanding the cyber-victimization of young people: A test of routine activities theory

Research on cybervictimization focuses on a variety of behaviors. The present study focuses on four behaviors: hacking, having obscene photos shared, bullying, and stalking/trespass to test the Lifestyles Routine Activities Theory (LRAT). Much of the research on cybervictimization uses LRAT to help explain how some groups of individuals are susceptible to becoming victims. We surveyed young adults, aged 18–25, using a paid Qualtrics sample and a convenience sample from a southern university. Using binominal logistic regression, we test the likelihood of victimization across the various behaviors and with the three main elements of LRAT, motivated offender, suitable target, and absence a capable guardian. We found that online dating was the most likely way to be exposed to a motivated offender, that visiting explicit websites made one a suitable target, and knowing how to set privacy settings helped guard against victimization.

A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks

Privacy leakage resulting from information sharing in online social networks (OSNs) is a serious concern for individuals. One of the culprits behind the problem is that existing privacy policies developed for OSNs are not fine-grained or flexible enough, resulting in privacy settings that could hardly meet the privacy requirements of individuals. Neither would such privacy settings allow individuals to control where the information could go. In addition, there are hardly any effective mechanisms for measuring potential threats to privacy during information propagation. To alleviate the situation, in this paper, we propose a novel privacy-preserving information sharing scheme for OSNs in which information flow can be controlled according to the privacy requirements of the information owner and the context of the information flow. Specifically, we first formally define the privacy-dependent condition (PDC) for information sharing in OSNs and then design a PDC-based privacy-preserving information sharing scheme (PDC-InfoSharing) to protect the privacy of individuals according to the heterogeneous privacy requirements of individuals as well as the potential threats that they may face. Furthermore, to balance information sharing and privacy protection, the techniques of reinforcement learning is utilized to help individuals reach a trade-off. PDC-InfoSharing would allow the privacy policies for specific information audience to be derived based on PDC to achieve dynamical control of the flow of information. Theoretical analysis proves that the proposed scheme can assist individuals in adopting fine-grained privacy policies and experiment shows that it can adapt to different situations to help individuals achieve the trade-off between information sharing and privacy protection.

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.

Navigating Privacy Concerns: Social Media Users' Perspectives on Data Sharing

This study aims to explore social media users' perspectives on privacy concerns and data sharing, identifying the major themes that encapsulate their experiences and attitudes towards online privacy and the strategies they employ to protect their personal information. A qualitative research design was employed, involving semi-structured interviews with 23 social media users from diverse demographic backgrounds. Thematic analysis was used to analyze the interview transcripts, focusing on identifying and interpreting patterns related to privacy concerns and protective behaviors. The analysis revealed five major themes: Understanding of Privacy, Attitudes Towards Data Sharing, Privacy Management Strategies, Impact of Social Media on Privacy, and Future Perspectives on Privacy. Each theme comprised several categories and concepts, including Definitions of Privacy, Privacy Awareness, Willingness to Share, Risks and Benefits, Use of Privacy Settings, Information Disclosure, Avoidance Behaviors, Perceived Threats, Changes in Behavior, Desired Changes, Predictions about Privacy, and User Empowerment. The study highlights the complex and multifaceted nature of social media users' privacy concerns and the strategies they adopt to navigate these challenges. It underscores the importance of developing more intuitive privacy controls and the need for ongoing education on privacy management. The findings also suggest a call for social media platforms and policymakers to consider users' diverse needs and concerns in the development of privacy protection measures.

Determining The Internet Risk Awareness of The Parents

With the rapid developments in science and technology, today's age has begun to be called the "technology age". With the rapid development of technology, technological devices and innovations have started to take an important place in people's lives in this age. So much so that most innovations have now become a way of life, a way of life for individuals. Undoubtedly, one of the innovations that has become a way of life for people and that has affected their lives to such a great extent has been the "Internet", which enables communication through technological devices. Internet, personal computers, phones, tablets, etc. emerges as a communication network that provides global communication with smart technological devices. Although the Internet has introduced many innovations into our lives, it also contains some risks. This research was carried out to determine the internet risk awareness of parents. For this purpose, the interview form was used. In addition to the questions revealing the descriptive characteristics of the parents in the form, there are questions about whether there are problems encountered in the internet environment and what measures are taken for the problems encountered. In this direction, 20 parents of students studying at a private college in Konya were interviewed. Qualitative research method was used to analyze the obtained data. In the study in which content analysis was used, it was determined that all of the parents used smart phones and used the internet for less than 3 hours a day. In addition, it was concluded that the majority of parents have social media accounts. In the research, it has been determined that not all of the parents have encountered any health-related problems on the internet, but some parents have encountered problems in the social media usage process, communication, banking and trade areas on the internet. In the research, it was determined that the parents resorted to security measures such as "uninstalling the application on the phone, not accepting notifications from unknown accounts, applying to cyber security, keeping privacy settings up-to-date, blocking the person, changing the password" for the problems they encountered during the social media usage process. In the research, it has been determined that the parents who encounter problems with communication on the internet resort to "blocking uncontrolled advertisements, blocking offers from unknown accounts, keeping information safe and not accepting messages from unknown accounts". In the research, it has been determined that the parents who encounter problems with banking transactions on the internet resort to security measures such as "contacting the bank, installing a virus program on their phone, taking other measures regarding banking transactions, and calling the bank by not entering their account". Finally, in the research, it has been determined that the parents who encounter problems related to commerce or shopping transactions on the internet resort to security measures such as "complaining to the authorities about the problem, choosing reliable sites, checking whether the sites are 3D reliable, and switching to virtual card application".

A novel privacy protection scheme for internet of things based on blockchain and privacy set intersection technique

In the era of big data, an ocean of data generated by Internet of Things (IoT) devices will be analyzed and processed by cloud computing. However, outsourcing of data can lead to leakage of user privacy to those unreliable service providers. In this paper, we propose a novel privacy-preserving scheme for IoT device by employing privacy set intersection (PSI) and blockchain technique to achieve data privacy. First, a homomorphic encryption PSI technique based on 0-1 encoding is proposed, which well hides the set base to ensure data privacy. Second, combining blockchain structure and smart contract, the proposed scheme can improve the efficiency of data sharing by storing the shared data on a blockchain. Third, the security analysis shows that the scheme has extremely high control over the individual data and can ensure the security and privacy of the data. Finally, we compare the functionality with other relevant schemes and demonstrate that our scheme functions well with low communication and computational overhead.

Privacy Protection Scheme for the Internet of Vehicles Based on Private Set Intersection

Performing location-based services in a secure and efficient manner that remains a huge challenge for the Internet of Vehicles with numerous privacy and security risks. However, most of the existing privacy protection schemes are based on centralized location servers, which makes them all have a common drawback of a single point of failure and leaking user privacy. The employment of anonymity and cryptography is a well-known solution to the above problem, but its expensive resource consumption and complex cryptographic operations are difficult problems to solve. Based on this, designing a distributed and privacy-secure privacy protection scheme for the Internet of Vehicles is an urgent issue for the smart city. In this paper, we propose a privacy protection scheme for the Internet of Vehicles based on privacy set intersection. Specially, using privacy set intersection and blockchain techniques, we propose two protocols, that is, a dual authentication protocol and a service recommendation protocol. The double authentication protocol not only ensures that both communicating parties are trusted users, but also ensures the reliability of their session keys; while the service recommendation protocol based on pseudorandom function and one-way hash function can well protect the location privacy of users from being leaked. Finally, we theoretically analyze the security that this scheme has, i.e., privacy security, non-repudiation, and anti-man-in-the-middle attack.

Online taxi users' optimistic bias: China youths' digital travel and information privacy protection.

Digital travel platforms not only provided people with convenient travel but also raised a series of problems regarding information privacy protection. In order to analyze privacy protection behavior, this study surveyed 441 subjects aged 18-35 who utilized digital travel platforms based on a structural model of protective motivation theory. The results indicated that a perceived threat, self-efficacy, and response efficacy positively and significantly impacted youths' privacy concerns. Furthermore, privacy concerns were positively related to privacy protection behavior and were an intermediate variable between the relationships among perceived threat, self-efficacy, response efficacy, and privacy protection behavior. This study identified the moderating effect of youths' knowledge of platform privacy settings on the relationship between privacy concerns and protection behavior. In addition, the results confirmed that an optimistic bias did exist among talented youth with high privacy knowledge in terms of a practical level of privacy management. These unique findings represent the exceptional contributions and innovation points of this study.

Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations. Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram, we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings. Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool “JTR,” we can iterate through the entire worldwide mobile phone number space in < 150 s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest. Regarding mitigations, we most notably propose two novel rate-limiting schemes: our incremental contact discovery for services without server-side contact storage strictly improves over Signal’s current approach while being compatible with private set intersection, whereas our differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information.

Silence on Social Media: Factors Shaping Chinese International Students’ Decision on Speaking up on Social Media

ABSTRACT Chinese international students represent a distinct group for social media studies. The present research investigates the social media platforms they choose to express their opinions by conducting a phenomenological interview of 20 Chinese international students from different universities in the U.S. The result shows that the experience of studying abroad causes Chinese international students to have opinions that differ from those of the major users of Chinese social media; they prefer to express their opinions on Chinese social media; technological issues such as privacy settings and functions influence their choice; and, differences in gratifications lead to different choices of social media platforms.

Influence maximization under limited network information: seeding high-degree neighbors

The diffusion of information, norms, and practices across a social network can be initiated by compelling a small number of seed individuals to adopt first. Strategies proposed in previous work either assume full network information or a large degree of control over what information is collected. However, privacy settings on the Internet and high non-response in surveys often severely limit available connectivity information. Here we propose a seeding strategy for scenarios with limited network information: Only the degrees and connections of some random nodes are known. This new strategy is a modification of ‘random neighbor sampling’ (or ‘one-hop’) and seeds the highest-degree neighbors of randomly selected nodes. Simulating a fractional threshold model, we find that this new strategy excels in networks with heavy tailed degree distributions such as scale-free networks and large online social networks. It outperforms the conventional one-hop strategy even though the latter can seed 50% more nodes, and other seeding possibilities including pure high-degree seeding and clustered seeding.

A Dynamic and Automated Access Control Management System for Social Networks

In recent years, online social networks (OSNs) have become an essential part of our social life. In OSNs, users can post resources to predefined groups of users, for example, family, friends, close friends. However, due to these predefined groups of users, few irrelevant users may get access to these published resources. Moreover, the users cannot configure privacy settings due to the lack of technical knowledge and the rigidity of the access control management system. To tackle these issues, we propose a text-based dynamic and fine-grain access control system for OSNs. Our proposed model uses a dynamic clustering algorithm to create user clusters based on the mutual interests of the users. After clustering, the proposed system creates automatic access rules based on the relationship between the users’ clusters and their resources. The proposed system will ensure fine-grained access control and automatic assignment of policies to the text-based resources. We have implemented our system to gauge the applicability, and the results are discussed in the experiments section.

Promoting Information Privacy Protection Awareness for Internet of Things (IoT)

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

JUST TECHNOPANIC OR A REAL RISK? PUBLISHING CHILDREN’S PICTURES ONLINE: A REVIEW OF LITERATURE

The internet has become an essential resource for social interaction among children, but it brings with it both advantages and disadvantages that depend in part on how it is used. This study, which is anchored in social learning theory, employed a desktop review of existing literature that focused on Kenya but covered global and other regional levels as well. The study found a number of benefits of the internet for children: updating family and friends on new developments in the children’s lives, reviewing photos and other records of past events, engaging in online interactions, and increasing their capacity for learning. Nonetheless, there are also internet-specific risks, such as access to inappropriate content and unsafe interactions with other children or adults. Other risks include “digital kidnapping” and contact with perpetrators who encourage children to engage in sexual activity. Although some countries have policies on internet usage, few have specific policies or guidelines addressing children’s vulnerability when sharing their pictures online. Moreover, most such policies are not applied in practice, especially in African countries. The study recommends developing and implementing policy frameworks to protect children online and using privacy settings to protect their information.

The paradox of declining fertility and declining contraceptive use in India: An artefact of survey design?

•Ensuring data quality in large scale surveys is challenging.•The trend and pattern of declining fertility and declining contraceptive use in India is puzzling.•Interview privacy setting and interviewer effect can partially explain the anomaly.•Large scale surveys impose severe demands on survey supervision and ability to ensure privacy.•Innovative ways of data collection for sensitive issues can be explored for proper reporting.

The Effect of Acute Stress on Privacy Decision-Making

Internet users’ digital privacy decisions are closely connected with their level of trust of information security, privacy concern, and perceived risks and benefits of internet use. One psychological factor that may potentially alter such privacy-related behaviors is acute stress, as it has shown to influence individual’s general decision-making. The goal of this between-subject designed research is to explore whether acute stress can affect decision-making in the digital privacy domain. Additionally, other privacy-related factors that may moderate this relationship, including trust and privacy fatigue, will be examined. We expect that participants who are exposed to acute stress will have poorer privacy decision-making outcomes, which will be represented by more frequent acceptance of online cookies and default privacy settings. Moreover, this relationship is expected to be magnified among individuals with low levels of Internet trust and those with high levels of privacy fatigue.