Disclosure Of Sensitive Information Research Articles

Attitudes towards disclosure of familial genetic risk in a Mediterranean island population – A survey of the Maltese population

Germline genetic testing has implications that extend beyond the individual patient to relatives, particularly for high-penetrance variants implicated in hereditary cancer or neurodegenerative syndromes. Many countries encourage patient-led communication to inform at-risk relatives, although the efficacy and uptake of this approach varies. Alternative scenarios envisage direct contact mediated by clinicians. The familial disclosure of sensitive genetic information is also determined by complex socio-ethnic factors. To date, no study has explored whether relatives would want to be informed of familial genetic risk and their preferences on different methods of communication in Malta. We thus used a published instrument that utilizes hypothetical scenario methodology to survey the attitudes of the Maltese population (n = 334) to receiving genetic information from family members. Two vignettes on Huntington's disease and colorectal cancer were presented. We also explored preferences towards the communication of genetic risk, confidentiality, and disclosure policies. Our preliminary results show that most respondents want to be informed of their increased risk by a family member or a clinician and would opt to receive confirmatory genetic testing. Most respondents preferred being informed of genetic risk by a close relative, but in the case of non-disclosure would want to be informed by a clinician. Most respondents expressed preference in favour of the introduction of registries, legislative change and sharing of contact details to address cases of nondisclosure. Our findings contribute further to evidence that supports, in selected hypothetical scenarios, an envisioned change in disclosure of genetic data policy by the public that is different from current practice to date.

The influence of communicating agent on users’ willingness to interact: A moderated mediation model

Empowered by AI, chatbots are increasingly being integrated to interact with users in one-on-one communication. However, academic scrutiny on the impact of chatbots on online interaction is lacking. This study aims to fill the gap by applying self-presentation theory (presenting the desired self-impression to others) to explore how the communicating agent (chatbot vs. human agent) in interactive marketing influences users’ interaction willingness, as well as the moderating roles of users’ public self-consciousness (sense of apprehension over self-presentation concern) and sensitive information disclosure (private information linked to an individual). The results of three experimental studies indicate that chatbots can improve users’ willingness to interact by mitigating the self-presentation concern. Further, users’ public self-consciousness and sensitive information disclosure moderated the impact of chatbots in online interactions. These effects were particularly impactful for users with higher public self-consciousness and in situations with sensitive information disclosure. The findings provide theoretical and practical implications for human-chatbot interaction, chatbot strategy, and the application of chatbots in online communication.

A systematic approach for threat and vulnerability analysis of unmanned aerial vehicles

The proliferation of Unmanned Aerial Vehicles (UAVs) is expected to experience a substantial rise in the next years, driven by their ever-increasing application across various domains. However, ensuring secure communication between UAVs and their ground stations is crucial to prevent the unauthorized disclosure of sensitive information that could jeopardize the mission’s integrity when exploited by malicious actors. Despite this importance, several UAV systems currently operate based on open-source command and control technologies, which have overlooked several security considerations while focusing on availability and safety. To address this concern, this study conducts a comprehensive security assessment of UAV-based systems starting with a systematic literature review whose main purpose is building a comprehensive catalog of threats associated with this technology. Particular attention has been paid to the MAVlink protocol, an open-source protocol commonly utilized for telemetry and command and control for multiple UAVs. Therefore, drawing upon the built catalog, a threat modeling and penetration testing technique has been employed to examine the MAVlink implementation on a real UAV. A threat model developed for a specific case study is also presented, leading to the discovery of four new vulnerabilities, some of which were successfully exploited through attacks. By shedding light on these vulnerabilities, this work seeks to encourage further investigation and research to develop robust security mechanisms for UAV communication systems. It is imperative to address these vulnerabilities proactively to enhance the overall security posture and safeguard against potential threats in the UAV ecosystem.

شروط السر الطبي بين الشريعة والقانون

This study addresses a delicate and profound concern about the intricate dynamics between the medical practitioner's professional responsibilities and the patient's need for privacy. The symbiotic nature of the doctor-patient relationship necessitates the disclosure of sensitive information, even classified as secrets, to the physician. However, there exists a paradoxical scenario where the patient desires to shield confidential information from the doctor, notwithstanding the professional obligation to disclose such details. Legal provisions further complicate this dynamic, delineating instances where divulging secrets is permissible. The convergence of the patient's interest and the broader societal welfare underscores the complexity of the matter, prompting a careful balance between maintaining confidentiality and averting potential harm. Islamic law, recognizing the gravity of safeguarding personal secrets, prohibits revealing such information unless there is an overriding imperative to do so. Fundamentally, the essence of secrecy rests on the confidentiality of the information and the patient's explicit desire to prevent its exposure. The realm of medical practice, encompassing procedures like examinations, laboratory tests, and X-ray imaging, inherently involves confidential information. In delving into pertinent legal literature, specific criteria emerge, elucidating the conditions under which information qualifies for classification as medical confidentiality. These criteria include the intrinsic nature of the information and its significance in the context of preserving the sanctity of the doctor-patient relationship.

A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network.

Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss. Secondly, use the layer-wise relevance analysis method to measure relevance difference between neurons weight and outputting results. Deep-DPGI disturbs neuron weight by adaptive noising mechanism, protecting the safety of high-order gene interactions and balancing the privacy and utility. Specifically, more noise is added to gradients of neurons that is less relevance with the outputs, less noise to gradients that more relevance. Finally, Experiments on simulated and real datasets demonstrate that Deep-DPGI not only improve the power of high-order gene interactions detection in with marginal and without marginal effect of complex disease models, but also prevent the disclosure of sensitive information effectively.

Privacy-Preserving Push-Sum Average Consensus via State Decomposition

Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in order to reach an agreement. Under a general average consensus algorithm, information exchanged through wireless or wired communication networks could lead to the disclosure of sensitive and private information. In this paper, we propose a privacy-preserving push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. Each node decomposes its initial state arbitrarily into two substates, and their average equals to the initial state, guaranteeing that the agent's state will converge to the accurate average consensus. Only one substate is exchanged by the node with its neighbours over time, and the other one is reserved. That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage. Different from the existing state-decomposition approach which only applies to undirected graphs, our proposed approach is applicable to strongly connected digraphs. In addition, in direct contrast to offset-adding based privacy-preserving push-sum algorithm, which is vulnerable to an external eavesdropper, our proposed approach can ensure privacy against both an honest-but-curious node and an external eavesdropper. A numerical simulation is provided to illustrate the effectiveness of the proposed approach.

A Differentially Private Method for Distributed Optimization in Directed Networks via State Decomposition

In this paper, we study the problem of consensus-based distributed optimization, where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimization over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is not shared. That is to say, only one substate is visible to an adversary, protecting the sensitive information from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.

Did anonymity in self-administered questionnaires improve disclosure of sensitive information during the 2022 mpox outbreak in England? An observational study

BackgroundBetween May 6, 2022, and Jan 16, 2023, 3555 mpox cases were reported in England, predominantly in gay, bisexual, and other men who have sex with men. Initially, the UK Health Security agency administered questionnaires to laboratory-detected cases via telephone calls. From June, 2022, cases were requested by text or email to complete the questionnaire online, with optional anonymous completion. To inform future approaches, we assess whether anonymity improved disclosure of sensitive information. MethodsIn this observational study we analysed questionnaire data completed by people with a laboratory-detected case of mpox. We included questionnaires that were completed from May 25, 2022, to Jan 16, 2023, and restricted them to anonymous or identifiable self-completed responses. Questionnaires with forename, surname, and birth date, or an ID emailed to participants, which therefore could link to laboratory data, were considered identifiable. Questionnaires without any personal identifiable information were considered anonymous. We compared the responses to seven sensitive risk factor or exposure questions using Pearson's χ2. FindingsAll 3555 people diagnosed with mpox infection in England were invited to complete the questionnaire through either phone call or web link.We obtained 1075 (30%) completed questionnaires, with a response rate decreasing from 45% in May to 20% in July 2022. We included 531 self-completed questionnaires in this analysis, of which 259 (49%) were anonymous and 272 (51%) were identifiable. The median age of participants was 39 years, with 514 (97%) men, 12 (2%) women, and five (1%) other. The largest ethnic groups were white (79%; n=422) and mixed or multiple ethnic groups (9%; n=47). Results of all seven questions were similar: 98% (n=254/259) of anonymous and 97% (n=265/272) of identifiable cases answered all seven questions, 49% (n=127) and 54% (n=147) reported a sexually transmitted infection diagnosis in the past 12 months (p=0·2), 24% (n=63) and 27% (n=73) reported ten or more sexual partners in the past 3 months (p=0·8), and 15% (n=38) and 18% (n=50) reported knowing another person with mpox infection (p=0·5), respectively. InterpretationTransitioning to self-completed questionnaires resulted in reduced uptake, although optional anonymity possibly prevented a steeper drop. Anonymity did not appear to affect reporting of sensitive information, specifically of sexual behaviours or history associated with mpox risk, which reinforces results of previous literature. Our interpretation is limited, however, by relatively low questionnaire uptake, and by only analysing reported rather than true risk. The decision to implement anonymous questionnaires should therefore weigh the potential benefits of increased uptake against the disadvantage of restricted data linkage. FundingNone.

Consent, assent and randomised evaluations

Background:Randomised trials have been on the rise in social policy over the last decade and a half, particularly in areas working with young people and vulnerable adults. Informed consent is an important principle for ethics committees governing research conducted by universities. Aims and objectives:We consider the arguments for and against opt-in consent by parents, and opt-out assent, when it comes to trials taking place, particularly in schools. Methods:We review what is known about this from a methodological standpoint. Findings:We find that extant evidence suggests that requiring opt-in consent, rather than assent, to participation, risks reducing the ethical standards of trials by minimising participation; and by potentially risking disclosure of sensitive information about a child’s life to their parents. Moreover, there are important equity considerations, with more vulnerable groups likely to be excluded from research findings under an opt-in framework. Discussion and conclusion:We conclude that the ethical argument for assent rather than consent is compelling under some circumstances, and should be considered on a case-by-case basis. Precautions must always be taken to safeguard participants.

AI Chatbots And Psychotherapy: A Match Made In Heaven?

Dear Editor, Artificial Intelligence (AI) is revolutionizing psychotherapy by combating its inaccessibility.1 AI chatbots and conversational agents are among the most promising integrations in psychiatry and psychology. By harnessing the power of machine learning and artificial intelligence (Figure I), AI chatbots and conversational agents can provide a level of interaction and support previously unattainable in the landscape of mental health. In this letter, I will explore some of their numerous benefits. Chatbots have evolved from early examples such as ELIZA, a programmable natural language processing program, and PARRY, a program that mimicked the behaviour of a human with schizophrenia, to modern ones such as Woebot, Tess, and Replika, which offer cognitive behavioural therapy, sessions, and self-understanding, respectively. Some chatbots such as Kaspar and Nao are also embodied in physical robots, which help children with autism spectrum disorder (ASD) learn social skills and facial recognition. AI chatbots can address the challenges of mental health care, such as the shortage of professionals2, especially in underdeveloped countries; the stigma of seeking help, which is prevalent in many cultures; the cost of therapy, which can be prohibitive for many people; and the limitations of human therapists, who can have variable attention spans, attention-to-detail, and memory capacities. AI chatbots can also offer benefits such as accessibility, anonymity, personalization, and effectiveness. Studies have shown that AI chatbots can improve the outcomes and experiences of mental health patients in various ways, such as enhancing the performance of those with ASD with robotic "therapists"1, increasing their disclosure of sensitive information to chatbots2, and reducing their distress after talking about their problems with conversational agents3. The benefits of AI chatbots for mental health are clear. However, they also face many challenges that limit their usability and reliability. They may lack the emotional skills of human therapists and may not be accountable for their diagnoses and recommendations. They may also not be based on sound mental health knowledge and evidence, as technology companies may not have expertise and experience in this field. Therefore, technology companies and mental health professionals must collaborate and adopt a rational approach to develop and evaluate chatbots. Only then can chatbots realize their full potential and complement existing mental health services safely and ethically.

Local Differential Privacy Based Membership-Privacy-Preserving Federated Learning for Deep-Learning-Driven Remote Sensing

With the development of deep learning, image recognition based on deep learning is now widely used in remote sensing. As we know, the effectiveness of deep learning models significantly benefits from the size and quality of the dataset. However, remote sensing data are often distributed in different parts. They cannot be shared directly for privacy and security reasons, and this has motivated some scholars to apply federated learning (FL) to remote sensing. However, research has found that federated learning is usually vulnerable to white-box membership inference attacks (MIAs), which aim to infer whether a piece of data was participating in model training. In remote sensing, the MIA can lead to the disclosure of sensitive information about the model trainers, such as their location and type, as well as time information about the remote sensing equipment. To solve this issue, we consider embedding local differential privacy (LDP) into FL and propose LDP-Fed. LDP-Fed performs local differential privacy perturbation after properly pruning the uploaded parameters, preventing the central server from obtaining the original local models from the participants. To achieve a trade-off between privacy and model performance, LDP-Fed adds different noise levels to the parameters for various layers of the local models. This paper conducted comprehensive experiments to evaluate the framework’s effectiveness on two remote sensing image datasets and two machine learning benchmark datasets. The results demonstrate that remote sensing image classification models are susceptible to MIAs, and our framework can successfully defend against white-box MIA while achieving an excellent global model.

Privacy-Preserving Solution for European Union Digital Vaccine Certificates

The recent COVID-19 pandemic situation highlights the importance of digital vaccine certificates. In response, the European Union (EU) developed EU Digital Vaccine Certificates to enable proof of non-infectivity and completed vaccinations. However, these solutions suffer from several shortcomings, such as ineffective certificate holder identification and a high violation of user privacy with the disclosure of sensitive information. In this work, we present a novel solution for privacy-preserving EU Digital Vaccine Certificates. Our solution solves the aforementioned privacy and security shortcomings and is in line with current EU legislation, i.e., the General Data Protection Regulation (GDPR), the upcoming revision of the electronic IDentification, Authentication, and trust Services (eIDAS), called regulation eIDAS 2.0, and the new tools that it envisages to be led by European digital identity. This identity is intended to allow citizens to prove their identity to access online services, share digital documents, or simply prove specific personal characteristics such as age without revealing their identity or other personal information. The core of our proposal is built on our novel attribute-based credential scheme, which can be easily implemented on various handheld devices, especially on Android smartphones and smartwatches. However, due to the lightweight nature of our scheme, it can also be implemented on constrained devices such as smart cards. In order to demonstrate the security, privacy, and practicality inherent in our proposal, we provide the security analysis of the cryptographic core along with a set of experimental results conducted on smartphones and smart cards.

A Certificateless Anonymous Authentication Scheme for VANETs Based on Ring Signature

There is a potential risk of privacy issues due to the disclosure of sensitive information about vehicles in Vehicular ad hoc networks (VANETs), including their real identities and driving trajectories. Anonymous authentication is an effective method to solve the above problems. Ring signature, a cryptographic primitive, offers the ability to create anonymous authentication schemes wherein the verifier can verify the signer’s legitimacy without having knowledge of their actual identity. Researchers have proposed several schemes that aim to protect privacy and maintain anonymity using ring signatures. However, a major drawback of most of these schemes is the linear correlation between the length of the signature and the number of ring members. Our proposed solution involves the construction of a certificateless anonymous authentication scheme that utilizes ring signature with a significantly reduced signature size, which follows a logarithmic relationship with the number of ring members. Through our analysis and comparison, it can be concluded that the privacy information of vehicles is well protected in our scheme, and the communication cost is significantly reduced compared with the existing protocols.

Using Data Hazards to support safe and ethical digital footprint research

Introduction & BackgroundHealth research using digital footprint data often involves the collection and use of large datasets that contain deeply personal information to make inferences about the course and onset of illness. In this context, innovating responsibly is essential for the field to develop safe, trustworthy and, ultimately, ethical research.
 The inherent interdisciplinarity of digital footprints research can be a challenge to this aim, with different fields having different ethical norms and standards. As well as this, there has been a strong focus to date on traditional ethical issues such as privacy, which do not necessarily account for the breadth of issues that arise in data science and internet-based work.
 Objectives & ApproachData Hazards is an open-source project that aims to provide a controlled vocabulary of ethical risks (Data Hazards) that can arise from data science research and its implementation. This vocabulary is presented as a set of 11 Hazard labels (v1.0) each with a visual icon and a set of safety precautions.
 Over three events in 2021-2022 we invited feedback from researchers who volunteered to take part in a Data Hazards workshop (N=15). They varied from PhD students to professors and worked across a range of disciplines, and were asked to discuss the case of mental health prediction from Twitter.
 Relevance to Digital FootprintsSince digital footprint technologies have great potential to pave the way for earlier and more personal medical treatment, it is important for researchers to be able to innovate whilst considering and communicating risk. We can then collaborate to establish effective safety precautions that allow us to maintain research momentum, without compromising safety or trust.
 ResultsBased on discussion at the workshops and surveys completed by participants, four main Data Hazards were raised for consideration by the digital footprint research community. These were: 'Lack of Community Involvement' relating to the need to further involve those with lived experience in the development of new technologies; 'Reinforces Existing Bias' due to the potential for automated labelling of ground-truth data to bias training datasets; 'Privacy' given the potential disclosure of sensitive information without consent; and 'Danger of Misuse' due to strong potential for malicious use of such technologies.
 Other considerations included the potential psychological risk to those labelling suicide and self-harm content with limited support.
 Conclusions & ImplicationsThe Data Hazards identified provide a means of communicating and clarifying ethical concerns so that they can be more easily addressed in this complex and multidisciplinary field. Further collaboration by the research community to develop and agree appropriate safety precautions would help to build trust in these new technologies before they are deployed in practice.

FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems.

In traditional centralized Android malware classifiers based on machine learning, the training sample uploaded by users contains sensitive personal information, such as app usage and device security status, which will undermine personal privacy if used directly by the server. Federated-learning-based Android malware classifiers have attracted much attention due to their privacy-preserving and multi-party joint modeling. However, research shows that indirect privacy inferences from curious central servers threaten this framework. We propose a privacy risk evaluation framework, FedDroidMeter, based on normalized mutual information in response to user privacy requirements to measure the privacy risk in FL-based malware classifiers. It captures the essential cause of the disclosure of sensitive information in classifiers, independent of the attack model and capability. We performed numerical assessments using the Androzoo dataset, the baseline FL-based classifiers, the privacy-inferred attack model, and the baseline methodology of privacy evaluation. The experimental results show that FedDroidMeter can measure the privacy risks of the classifiers more effectively. Meanwhile, by comparing different models, FL, and privacy parameter settings, we proved that FedDroidMeter could compare the privacy risk between different use cases equally. Finally, we preliminarily study the law of privacy risk in classifiers. The experimental results emphasize the importance of providing a systematic privacy risk evaluation framework for FL-based malware classifiers and provide experience and a theoretical basis for studying targeted defense methods.

A Systematic Review of Blockchain-based Identity Management Solutions

The involvement of digital identity in almost all online services contributes to the growing reliance on Identity Management Systems (IDMS) that establish, verify, and manage digital identities. However, digital identities are still kept in central repositories. Which are controlled by a single authority that may have many vulnerabilities due to low security, leading attackers to exploit these vulnerabilities and causing various security breaches such as identity theft or disclosure of sensitive information. Additionally, powerful entities who have access to these repositories, could gather and abuse users' information without their knowledge or consent. The concept of Self-sovereign Identity (SSI) allows users to exert ownership of their identity and gain insight into how their data is being used. The development of Blockchain technology has made a breakthrough in achieving SSI by giving individuals the ability to be the final arbiter of who can access and use their own identity. This paper overviews the traditional identity management (IdM) models and presents the next generation of distributed IDMS using Blockchain technology that targets user-centricity and eliminates the identity provider as a trusted third party. Furthermore, It gives an analysis of the recent Blockchain-based IdM solutions, discussing their architecture, components, and features. It also, reveals their weaknesses to identify the gaps between these solutions for future secure IDMS.

EXAMINING THE ETHICAL PERCEPTIONS OF ACCOUNTING STUDENTS IN PREPARING FINANCIAL STATEMENTS

ABSTRACT
 
 Purpose – This study aims to determine the perceptions of accounting students on the ethics of preparing financial statements
 Design/methodology/approach – The type of data in this study is primary data with the data collection technique used is a questionnaire. The research sampling technique used simple random sampling.
 Findings – The results showed that there was no difference in the perceptions of accounting student class 2018 and accounting student class 2019 regarding earnings management factors, disclosure of sensitive information, costs and benefits, and accountability. There are differences in the perceptions of accounting student class 2018 and accounting student class 2019 about the factor of misstatement. This is because accounting student class 2018 have a lower tendency to misstate financial statements compared to accounting student class 2019.
 Originality – The samples in this study were 300 respondents, 150 students from class 2018 and 150 students from class 2019.
 Keywords: Earnings Management, Misstatement, Disclosure of Sensitive Information, Costs and Benefits, Accountability
 Paper Type Research Result

Peer Effect on Environmental Information Disclosure: Evidence from High-polluting Industries in China

Background: Among the research on the influencing factors of environmental information disclosure, scholars rarely identify the factors from the perspective of the enterprise’s external environment, especially peer enterprise behavior. In fact, the disclosure of environmental information by most enterprises in China is still only voluntary, and the form of disclosure is so chaotic that it is easy to be influenced by other enterprises. Objective: This study aimed to determine whether a firm's EID is affected by peer firms and contribute to the existing literature on the influencing factors of EID. Methods: An analytical framework incorporating the herd behavior hypothesis, the legitimate theory, and the stakeholder theory is constructed, and fixed effect estimation, as well as a two-stage least square, is used to test the hypotheses. Results: There is a peer effect on environmental information disclosure of high-polluting firms. It has been observed that the focal firm imitates the disclosure behavior of small peer firms more than the large peer firms. Moreover, a peer effect of environmental disclosure on sensitive and non-sensitive information is also reported, but the peer effect on sensitive information is larger than that on nonsensitive information. Conclusion: First, policymakers need to realize that there is a peer effect involved in EID among highpolluting firms and improve the binding force of environmental regulations. Second, there are “demonstration effects” involved in EID. In the practice of regulations on disclosure, the smaller firms need to be under stricter scrutiny and set as models of EID to improve the efficiency of supervision and regulation. Third, enterprises have a stronger peer effect on the disclosure of sensitive information. Governments should strengthen the supervision of sensitive information disclosure.

A privacy‐preserving method for publishing data with multiple sensitive attributes

AbstractThe overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

Using Observational Dyadic Methods in Youth Mentoring Research: Preliminary Evidence of the Role of Actors’ and Partners’ Self-disclosure in Predicting Relationship Quality

Self-disclosure builds high quality relationships, but knowledge of self-disclosure in youth mentoring relationships is limited by a lack of research and reliance on self-reports. To demonstrate the value of observational methods and dyadic modeling of mentoring communication processes, this study examined the associations between behavioral observation of self-disclosure and self-reported relationship quality in 49 mentee-mentor dyads (mentees: 73.5% female; x̄ age = 16.2, range = 12–19; mentors: 69.4% female; x̄ age = 36.2, range = 19–59). Video-recorded observations of disclosure were coded on three dimensions: amount (number of topics and detail of disclosure), intimacy (disclosure of personal or sensitive information), and openness (willingness to disclose). More intimate mentor disclosure was associated with higher mentee relationship quality, whereas higher amount of mentor disclosure combined with low intimacy was associated with lower mentee relationship quality. Greater mentee openness correlated with higher mentor relationship quality, but more intimate mentee disclosures were associated with lower mentee relationship quality. These preliminary findings illustrate the potential of methods that enable in-depth investigation of dyadic processes to advance understanding of how behavioral processes may influence mentoring relationships.