Privacy Domain Research Articles

GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants

Website privacy policies are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user-friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users’ questions about privacy policies. However, genAI’s reliability is a concern due to its potential for producing inaccurate information. This study introduces GenAIPABench, a benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench includes: 1) A set of curated questions about privacy policies along with annotated answers for various organizations and regulations; 2) Metrics to assess the accuracy, relevance, and consistency of responses; and 3) A tool for generating prompts to introduce privacy policies and paraphrased variants of the curated questions. We evaluated 3 leading genAI systems—ChatGPT-4, Bard, and Bing AI—using GenAIPABench to gauge their effectiveness as GenAIPAs. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.

On the Feasibility of Forgetting in Data Streams

In today's digital age, it is becoming increasingly prevalent to retain digital footprints in the cloud indefinitely. Nonetheless, there is a valid argument that entities should have the authority to decide whether their personal data remains within a specific database or is expunged. Indeed, nations across the globe are increasingly enacting legislation to uphold the "Right To Be Forgotten" for individuals. Investigating computational challenges, including the formalization and implementation of this notion, is crucial due to its relevance in the domains of data privacy and management. This work introduces a new streaming model: the 'Right to be Forgotten Data Streaming Model' (RFDS model). The main feature of this model is that any element in the stream has the right to have its history removed from the stream. Formally, the input is a stream of updates of the form (a, Δ) where Δ ∈ {+, ⊥} and a is an element from a universe U. When the update Δ=+ occurs, the frequency of a, denoted as f a , is incremented to f a +1. When the update Δ=⊥, occurs, f a is set to 0. This feature, which represents the forget request, distinguishes the present model from existing data streaming models. This work systematically investigates computational challenges that arise while incorporating the notion of the right to be forgotten. Our initial considerations reveal that even estimating F 1 (sum of the frequencies of elements) of the stream is a non-trivial problem in this model. Based on the initial investigations, we focus on a modified model which we call α-RFDS where we limit the number of forget operations to be at most α fraction. In this modified model, we focus on estimating F 0 (number of distinct elements) and F 1 . We present algorithms and establish almost-matching lower bounds on the space complexity for these computational tasks.

Disguise without Disruption: Utility-Preserving Face De-identification

With the rise of cameras and smart sensors, humanity generates an exponential amount of data. This valuable information, including underrepresented cases like AI in medical settings, can fuel new deep-learning tools. However, data scientists must prioritize ensuring privacy for individuals in these untapped datasets, especially for images or videos with faces, which are prime targets for identification methods. Proposed solutions to de-identify such images often compromise non-identifying facial attributes relevant to downstream tasks. In this paper, we introduce Disguise, a novel algorithm that seamlessly de-identifies facial images while ensuring the usability of the modified data. Unlike previous approaches, our solution is firmly grounded in the domains of differential privacy and ensemble-learning research. Our method involves extracting and substituting depicted identities with synthetic ones, generated using variational mechanisms to maximize obfuscation and non-invertibility. Additionally, we leverage supervision from a mixture-of-experts to disentangle and preserve other utility attributes. We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.

Performance optimization of multipath K-AOMDV protocol using SVM against blackhole attack

<p>This research focuses on the Ad Hoc On-Demand Multi-Path Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in mobile ad hoc networks (MANETs). However, identifying attackers in such networks is a complicated task due to malicious nodes providing optimistic, forward-looking optimistic responses. In this study, the author proposes a novel security solution, the K-AOMDV (KNN- Ad Hoc On-Demand Multi-Path Routing protocol) that uses K-means clustering to prevent routing misbehaviour. The efficiency of the proposed K-AOMDV routing protocol is analyzed using supervised machine learning approach to predict optimal routes with minimal packet hops between nodes. The proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. It communicates the black hole attacker’s node identification (ID) into the network, ensuring that the attacker will not participate in the routing method in the future. The privacy domain in MANET is the main focus of this research, and the proposed solution affiliates an effective approach to enhancing the security of MANETs.</p>

Evaluating the Sustainable Competitive Advantage of Saudi e-Healthcare System: An Empirical Study.

This study seeks to investigate the various dimensions contributing to the sustainable competitive advantage of the Saudi eHealthcare system. A cross-sectional survey design was adopted in this study. Eleven factors were identified that may affect the sustainable competitive advantage, which were embedded in to the survey questionnaire. Participants included diverse healthcare workers (physicians, specialist physicians, nurses, managers) from Saudi public hospitals. A total of 382healthcare workers participated in the study. User experience and accessibility emerged as the most significant factor with a high mean of 4.04 ± 0.69, followed by innovation and continuous improvement (mean 3.8 ± 0.89) and AI adoption (mean 3.91 ± 0.82). Notably, a statistically significant difference is observed in the domain of Data Security and Privacy (p = 0.0448), with specialist physicians exhibiting a lower mean score (2.44 ± 0.86) compared to other roles. The need to strengthen overall competitiveness is emphasized by identified areas for improvement, including data security, interoperability, and integration, which highlight the necessity for specific initiatives.

Thematic exploration of online privacy literacy and examination of its future agenda

ABSTRACT In today’s digitized society of ever-increasing information sharing, Online Privacy Literacy (OPL) is not only important to feel safe but be safe as well. OPL refers to skills and knowledge of individuals that helps to protect their privacy online. OPL is crucial in the digital era, where extensive data collection poses privacy challenges. The lack of OPL is a major disadvantage as it may impair one’s privacy decision-making abilities such as whether and how much personal data to disclose online. Despite its importance, OPL research is in its early stages. To pave the way to bridge this gap, we conducted a systematic review of 57 articles, offering a theoretical connection to privacy research. Our synthesis highlights how OPL is defined, measured, and its primary knowledge domains. Seven themes emerged, emphasizing OPL across age groups and revealing unexplored areas like the interplay between OPL, technology acceptance, and technostress. This exploration provides a roadmap for researchers to understand OPL implications and its role in privacy domains, guiding future research directions. Experts can leverage this thematic exploration to develop knowledge, skills, and effective privacy protection measures for individuals and institutions in the dynamic landscape of online privacy.

Patient satisfaction with community pharmacy COVID-19 vaccination in Northern Ireland

Abstract Introduction Northern Ireland’s community pharmacies have contributed substantially to the COVID-19 response, with over 436,000 COVID-19 vaccines administered since March 2021. Community pharmacy has an established role across the UK in delivering influenza vaccination programmes. Research has found that community pharmacy vaccination can increase vaccine availability and uptake and is highly valued by patients1, and although European studies have demonstrated high satisfaction with community pharmacy COVID-19 vaccination services2, further studies are required that are applicable to a UK context. Aim This evaluation aimed to measure user satisfaction with the HSC community pharmacy COVID-19 vaccination service, reasons why users chose community pharmacy for vaccination, and user attitudes towards service quality in three key quality domains of patient-centredness, professionalism, and privacy. The findings aimed to inform recommendations for service improvement for future vaccination campaigns. Methods Following ethical approval from Keele University School of Pharmacy & Bioengineering Research Ethics Committee, a cross-sectoral survey of individuals accessing COVID-19 vaccination from 61 participating community pharmacies was carried out between 20th September 2022 and 3rd December 2022. Voluntary participation was offered to all individuals accessing COVID-19 vaccination from pharmacies purposively chosen as representative of the community pharmacy network. Respondent views on service quality and overall satisfaction were collected anonymously via a Microsoft Forms questionnaire. The results were analysed for overall satisfaction and across three quality domains of patient-centredness, professionalism and privacy identified from the literature as mattering most to users of pharmacy services3. The results were reported using descriptive statistics including frequencies, percentages, skewness and measurements of central tendency. The findings informed the development of five key recommendations for future service improvement. Results 135 patients completed the online questionnaire. Overall patient satisfaction was extremely high, with 100% of respondents indicating they would recommend the service to friends and family and that they would return to a community pharmacy for COVID-19 vaccination. 96% of participants cited convenience, trust, ease of access and time spent at the pharmacy as reasons they chose community pharmacy for vaccination. Respondents were very satisfied with the quality of the service provided across the domains of patient-centredness, professionalism and privacy. Over 95% of participants were very satisfied with the quality of the service, professionalism of the vaccinator and caring of the pharmacy team, however only 73.2% of respondents were very satisfied with the standard of the consultation room. Discussion/Conclusion Overall satisfaction with community pharmacy COVID-19 vaccination in Northern Ireland is very high. Participants largely chose to access vaccination from community pharmacies for reasons of convenience, trust, ease of appointment and time spent at the pharmacy. Users are very satisfied with service quality in terms of patient-centredness, professionalism and privacy. Although generalisation is limited due to relatively small sample size, underrepresentation of some geographical areas and the potential for selection bias arising from voluntary participation and use of online questionnaires, this evaluation still provides a framework for continued quality improvement and a detailed perspective on user attitudes towards community pharmacy COVID-19 vaccination that has informed the development of recommendations for service improvement.

Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

Anonymization techniques are widely used to make personal data broadly available for analytics/data-mining purposes while preserving the privacy of the personal information enclosed in it. In the past decades, a substantial number of anonymization techniques were developed based on the famous four privacy models such as k-anonymity, ℓ-diversity, t-closeness, and differential privacy. In recent years, there has been an increasing focus on developing attribute-centric anonymization methods, i.e., methods that exploit the properties of the underlying data to be anonymized to improve privacy, utility, and/or computing overheads. In addition, synthetic data are also widely used to preserve privacy (privacy-enhancing technologies), as well as to meet the growing demand for data. To the best of the authors’ knowledge, none of the previous studies have covered the distinctive features of attribute-centric anonymization methods and synthetic data based developments. To cover this research gap, this paper summarizes the recent state-of-the-art (SOTA) attribute-centric anonymization methods and synthetic data based developments, along with the experimental details. We report various innovative privacy-enhancing technologies that are used to protect the privacy of personal data enclosed in various forms. We discuss the challenges and the way forward in this line of work to effectively preserve both utility and privacy. This is the first work that systematically covers the recent development in attribute-centric and synthetic-data-based privacy-preserving methods and provides a broader overview of the recent developments in the privacy domain.

A descriptive study to assess the experiences of mothers of infants (42 days-6 months of age) on respectful maternity care residing in Dadu Majra Colony, UT, Chandigarh (2021-2022)

Respectful Maternity Care (RMC) is right to receive dignified and respectful care by health care worker and right to freedom from harm. “The relationship between the lack of quality care and adverse maternal outcome has been highlighted globally. RMC include respect for women autonomy, dignity, feelings, privacy choices and freedom from ill-treatment, coercion, consideration for personal preference include option for companionship during maternity care.” To assess the experiences of mothers of infants (42 days- 6 months of age) regarding RMC residing in Dadu Majra colony UT. Chandigarh. This descriptive study was conducted on 133 mother of infants (42 days-6 month of age) residing in one semi urban area of Chandigarh, by using total enumeration sampling technique. The ethical clearance was given by the Ethics committee of NINE, PGIMER, Chandigarh. Tool used for data collection was interview schedule comprised of three parts : 1) Socio demographic profile of participants 2) Obstetrical profile of participants 3) Interview schedule to assess RMC, in the form of Likert scale in which 43 items scored into 5 categories i.e All the times (5). Sometimes (4). Often (3), Rarely (2) and Never (1). Data was collected by interviewing the participants as per interview schedule after taking informed written consent. Data was analysed by SPSS 20 version.Majority of the participants (90.2%) received very good RMC, other 7.3% received RMC and 2.3% received fair RMC. Most of the mothers received very good care in domain of dignified care (91.7%), equitable care (100.0%), highest attainable level of care (78.9%). freedom from harm (89.5%) and informed consent (88%) while the domain of privacy and confidential care 52.6% mothers received good respectful maternity care.Majority of the participant received the very good RMC, only few of the participants reported abusive cares such as yelling by the HCW and the security guards, slapped on the thighs etc.

Security and privacy research of Internet of things systems in mobile networks

The escalating integration of Internet of Things (IoT) devices with mobile networks presents unprecedented opportunities and challenges in the context of security and privacy. This paper offers a comprehensive overview of recent research focusing on the security and privacy concerns associated with IoT systems operating within mobile networks. The fundamental concepts, emerging threats, and potential solutions in this dynamic landscape are examined. The distinctive characteristics of IoT devices and their unique security and privacy requirements are outlined. The paper discusses inherent vulnerabilities, including limited computational resources, device heterogeneity, and the expansive attack surface resulting from interconnected devices. Additionally, the growing risks associated with data collection, transmission, and storage within mobile network environments are explored, emphasizing the importance of protecting user privacy and sensitive information. State-of-the-art security mechanisms and protocols designed to safeguard IoT systems in mobile networks are surveyed. Approaches such as encryption, authentication, access control, and intrusion detection systems are discussed as means to mitigate potential threats. The paper addresses the challenge of secure device onboarding and underscores the importance of ensuring the integrity and authenticity of IoT components within the network. Furthermore, the role of standards and regulatory frameworks in shaping security and privacy requirements for IoT systems is examined. Initiatives by organizations such as the Internet Engineering Task Force (IETF) and the European Union’s General Data Protection Regulation (GDPR) are discussed in relation to their influence on the development and deployment of secure IoT solutions. In conclusion, this review highlights the ongoing need for continuous research and development in the security and privacy domains of IoT systems in mobile networks. As the adoption of IoT devices and mobile network technologies continues to grow, maintaining vigilance in addressing evolving threats and ensuring compliance with emerging regulations is imperative. This paper aims to provide valuable insights into the evolving landscape of IoT security and privacy, catering to researchers, practitioners, and policymakers, and paving the way for a more secure and privacy-preserving future for IoT deployments in mobile networks.

Blockchain and 6G-Enabled IoT

Ubiquitous computing turns into a reality with the emergence of the Internet of Things (IoT) adopted to connect massive numbers of smart and autonomous devices for various applications. 6G-enabled IoT technology provides a platform for information collection and processing at high speed and with low latency. However, there are still issues that need to be addressed in an extended connectivity environment, particularly the security and privacy domain challenges. In addition, the traditional centralized architecture is often unable to address problems associated with access control management, interoperability of different devices, the possible existence of a single point of failure, and extensive computational overhead. Considering the evolution of decentralized access control mechanisms, it is necessary to provide robust security and privacy in various IoT-enabled industrial applications. The emergence of blockchain technology has changed the way information is shared. Blockchain can establish trust in a secure and distributed platform while eliminating the need for third-party authorities. We believe the coalition of 6G-enabled IoT and blockchain can potentially address many problems. This paper is dedicated to discussing the advantages, challenges, and future research directions of integrating 6G-enabled IoT and blockchain technology for various applications such as smart homes, smart cities, healthcare, supply chain, vehicle automation, etc.

Person-centered, high-quality care from a distance: A qualitative study of patient experiences of TelAbortion, a model for direct-to-patient medication abortion by mail in the United States.

Direct-to-patient telemedicine abortion allows people to receive mifepristone and misoprostol for medication abortion in their home without requiring an in-person visit with a healthcare provider. This method has high efficacy and safety, but less is known about the person-centered quality of care provided with telemedicine. We interviewed 45 participants from the TelAbortion study of direct-to-patient telemedicine abortion in the United States from January to July 2020. Semi-structured qualitative interviews queried their choices, barriers to care, expectations for care, actual abortion experience, and suggestions for improvement. We developed a codebook through an iterative, inductive process and performed content and thematic analyses. The experience of direct-to-patient telemedicine abortion met the person-centered domains of dignity, autonomy, privacy, communication, social support, supportive care, trust, and environment. Four themes relate to the person-centered framework for reproductive health equity: (1) Participants felt well-supported and safe with TelAbortion; (2) Participants had autonomy in their care which led to feelings of empowerment; (3) TelAbortion exceeded expectations; and (4) Challenges arose when interfacing with the healthcare system outside of TelAbortion. Participants perceived abortion stigma which often led them to avoid traditional care and experienced enacted stigma during encounters with non-study healthcare workers. TelAbortion is a high quality, person-centered care model that can empower patients seeking care in an increasingly challenging abortion context.

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.

Membership Inference Defense in Distributed Federated Learning Based on Gradient Differential Privacy and Trust Domain Division Mechanisms

Distributed federated learning models are vulnerable to membership inference attacks (MIA) because they remember information about their training data. Through a comprehensive privacy analysis of distributed federated learning models, we design an attack model based on generative adversarial networks (GAN) and member inference attacks (MIA). Malicious participants (attackers) utilize the attack model to successfully reconstruct training sets of other regular participants without any negative impact on the global model. To solve this problem, we apply the differential privacy method to the training process of the model, which effectively reduces the accuracy of member inference attacks by clipping the gradient and adding noise to it. In addition, we manage the participants hierarchically through the method of trust domain division to alleviate the performance degradation of the model caused by differential privacy processing. Experimental results show that in distributed federated learning, our designed scheme can effectively defend against member inference attacks in white-box scenarios and maintain the usability of the global model, realizing an effective trade-off between privacy and usability.

Information security and privacy in hospitals: a literature mapping and review of research gaps

ABSTRACT Information security and privacy are matters of concern in every industry. The healthcare sector has lagged in terms of implementing cybersecurity measures. Therefore, hospitals are more exposed to cyber events due to the criticality of patient data. Currently, little is known about state-of-the-art research on information security and privacy in hospitals. The purpose of this study is to report the outcome of a systematic literature review on research about the application of information security and privacy in hospitals. A systematic literature review following the PRISMA methodology was conducted. To reference our sample according to cybersecurity domains, we benchmarked each article against two cybersecurity frameworks: ISO 27001 Annex A and the NIST framework core. Limited articles in our papers referred to the policies and compliance sections of ISO 27001. In addition, most of our sample is classified by the NIST function “Protect,” meaning activities related to identity management, access control and data security. Furthermore, we have identified key domains where research in security and privacy are critical, such as big data, IOT, cloud computing, standards and regulations. The results indicate that although cybersecurity is a growing concern in hospitals, research is still weak in some areas. Considering the recrudescence of cyber-attacks in the healthcare sector, we call for more research in hospitals in managerial and non-technical domains of information security and privacy that are uncovered by our analysis.

Disguised as Privacy: Data Poisoning Attacks against Differentially Private Crowdsensing Systems

Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that the security domain and the privacy domain are typically considered separately. Therefore, it is unclear whether the defense methods in the privacy domain will have unexpected impact on the security domain. To bridge this gap, in this paper, we propose a novel Disguise-based Data Poisoning Attack (DDPA) against the differentially private crowdsensing systems empowered with the truth discovery method. Specifically, we propose a novel stealth strategy, i.e., disguising the malicious behavior as privacy behavior, to avoid being detected by truth discovery methods. With this stealth strategy, the shortcoming of failing to maximize the attack effectiveness is avoided naturally through structuring a bi-level optimization problem, which can be solved with the alternating optimization algorithm. Moreover, we show that the differentially private crowdsensing systems are vulnerable to data poisoning attacks, and enhancing the level of privacy will bring more serious security threats. Finally, the evaluation results on the real-world dataset Emotion and the synthetic dataset SynData demonstrate that DDPA can not only achieve maximum utility damage but also remain undetected.

Compartmentalised data protection in South Africa: The right to privacy in the Protection of Personal Information Act

In European Union (‘EU’) law, the entrenched right to data protection is an independent fundamental right. EU case law has gradually disconnected the right to data protection from the right to a private life. South Africa’s first exclusive data protection legislation, the Protection of Personal Information Act 4 of 2013 (‘POPIA’), is redolent of EU data protection legislation. However, the stated purpose of the POPIA is to give effect to the right to privacy. This article examines whether the laws of data protection can be wholly encapsulated within s 14 of the Constitution. To this end, this article considers two main conceptions of privacy in our law. The first is Neethling’s informational privacy and the reasonable expectation of privacy. The second is Rautenbach’s theory of informational control over personal matters in relation to other rights. On either approach, I argue that the substantive provisions of the POPIA are irreducible to privacy protection alone. Ultimately, framing the POPIA exclusively within the domain of privacy will either (i) unduly restrict legislative interpretation; or (ii) the true meaning of privacy will be diluted, leading to legal uncertainty. To avoid this, I suggest distinguishing between the value of privacy in the POPIA and the actual loss of privacy.

Dynamic Trust-Based Device Legitimacy Assessment Towards Secure IoT Interactions

Establishing trust-based interactions in heterogeneously connected devices appears to be the prominent mechanism in addressing the prevailing concerns of confidence, reliability and privacy relevant in establishing secure interactions among connected devices in the network. Trust-based assessment of device legitimacy is evolving given IoT devices’ dynamic and heterogeneous nature and emerging adversaries. However, computation and application of trust level in establishing secure communications, access control and privacy domain are rarely discussed in the literature. To compute trust, based on the quality of service, direct interactions, and the relationship between devices, we introduce a multi-factor trust computation model that considers the multiple attributes of interactions in an IoT network of heterogeneous devices providing a wide range of data and services. Direct trust is estimated for quality of service considering the response time, reliability, consistency, and integrity attributes of devices. The time decay factor influences the credibility of computed trust over time. The policy-driven mechanism is employed to sift the devices and isolate the malicious ones. Extensive simulations validate the proposed model’s effectiveness using Contiki’s Cooja simulator for IoT networks.

Teaching bioethics needs more than just a module: A comparison of knowledge and attitude of bioethics principles in 1st- and 3rd-year physiotherapy undergraduates

CONTEXT: Ethics is a building block of health-care practice. Medical ethics is values and behavior applied during patient care. It may be ingrained through culture, education, upbringing, and observation. Learning by observation during a clinical posting is a hidden part of the curriculum. With experiential learning, the students may change their attitude and behavior. Whether physiotherapy students have adequate knowledge of principles of bioethics and if it changes with clinical exposure is not known.AIMS: The aim of this study was to evaluate the perception of bioethics at entry level and post clinical exposure in physiotherapy students.SETTINGS AND DESIGN: This was a cross-sectional study carried out in physiotherapy school of tertiary care hospital.SUBJECTS AND METHODS: Sixty-five first-year (Group A) and 65 third-year (Group B) physiotherapy undergraduates participated after institutional ethical approval. The knowledge and attitude of medical ethics was evaluated using a prevalidated structured questionnaire.RESULTS: Descriptive analysis and responses of Group A and Group B were compared using Mann–Whitney U-test using SPSS 16 software. There was a felt need of more than 90% of students in both the groups for sound knowledge and training in bioethics. A statistically significant difference was obtained in domains of consent, privacy, confidentiality, honesty, and integrity between 1st- and 3rd- year students. There were mixed responses in principles of autonomy, shared decision-making, and professional boundaries. Many respondents were neutral and many statements showed mixed responses indicating lack of knowledge.CONCLUSIONS: The study concludes a need for sensitization in bioethics during clinical training to reinforce the principles in physiotherapy undergraduates beyond a formal module.

Learning From Older Adults to Promote Independent Physical Activity Using Mobile Health (mHealth)

Background: Healthy aging is critically important for several reasons, including economic impact and quality of life. As the population of older adults rapidly increases, identifying acceptable ways to promote healthy aging is a priority. Technologies that can facilitate health promotion and risk reduction behaviors may be a solution, but only if these mobile health (mHealth) tools can be used by the older adult population. Within the context of a physical activity intervention, this study gathered participant's opinions about the use of an mHealth device to learn about acceptance and to identify areas for improvement.Methods: The Independent Walking for Brain Health study (NCT03058146) was designed to evaluate the effectiveness of a wearable mHealth technology in facilitating adherence to a physical activity prescription among participants in free-living environments. An Exit Survey was conducted following intervention completion to gauge participant's perceptions and solicit feedback regarding the overall study design, including exercise promotion strategies and concerns specific to the technology (e.g., privacy), that could inform more acceptable mHealth interventions in the future. The Digital Health Checklist and Framework was used to guide the analysis focusing on the domains of Privacy, Access and Usability, and Data Management.Results: Participants (n = 41) were in their early 70's (mean = 71.6) and were predominantly female (75.6%) and White (92.7%). Most were college educated (16.9 years) and enjoyed using technology in their everyday life (85.4%). Key challenges included privacy concerns, device accuracy, usability, and data access. Specifically, participants want to know what is being learned about them and want control over how their identifiable data may be used. Overall, participants were able to use the device despite the design challenges.Conclusions: Understanding participant's perceptions of the challenges and concerns introduced by mHealth is important, as acceptance will influence adoption and adherence to the study protocol. While this study learned from participants at studycompletion, we recommend that researchers consider what might influence participant acceptance of the technology (access, data management, privacy, risks) and build these into the mHealth study design process. We provide recommendations for future mHealth studies with older adults.