Data Security Measures Research Articles

Protection Aware User Identity and Data Storage (PAUIDS) Scheme for Management of User Identification in Cloud

Cloud computing the technology which have the capability of modifying the method computing strongly, and storage resources will be accessed shortly. User Identification is an entity to detect the user who using the system or website. In information technology the protection of information consistently become a major issue to handle. The data might place in various locations in the world since it become particularly serious. The two main factors regarding cloud technology are information protection and security. The cloud operators can easily reach the sensitive information that affects the data security and protection measures. Therefore, this research protocol mainly focuses on secure data storage that always been a significant feature of quality of service. To guarantee the ‘rightness of users’ information in cloud storage system a Protection Aware User Identity and Data Storage (PAUIDS) algorithm is proposed that separates the document and independently stores the user information in the cloud storage servers. The proposed algorithm reduces the encryption and decryption time in a cloud storage system and providing secure and efficient data storage in cloud environments.

A Newly Proposed Prospective and Robust Computer Networking Model Architecture Based on the Infrastructure of Cloud Computing Contrivance

Abstract: Computer Networking Play’s a major role for data communication or data sharing and data transmissions from one location to another, which are geographically differ, but in today’s scenario where the main and primary major concerns are not to data transfer but also utilize all resources with greater efficiency and also preserves the confidentiality and integrity of the messages with respect to speed and time with lower Bandwidth and also consume a very low computational costs with low power supply and redirect to optimality. Cloud Computing also play’s a significant role to access data at geographically different locations. So In this paper we create a fusion of Computer Networking Architecture and Cloud Computing Architecture and released a very much superior fundamentally strong Cloud computing based Computer Networking model, which works on the concepts of ‘Virtualization’. Because when the number of hardware components (Servers) drastically increases all factors which are responsible to make possible networking among nodes are also consume each resources at extreme level, and networking becomes complex and slow, that’s why we used the concept of Virtual Machine. In this paper we proposed a Computer Networking model using the concepts of Cloud Computing. This model also suitable for data transmission but also take concern the most significant feature of Computer Networking, which is Data Security. This model also used some Proxy servers/ firewalls to take concern some security mechanisms. In this paper we also proposed Communication Oriented model among the Intercluster domains that how one node which belongs to another CLOUD cluster make possible communication among other InterCLOUD clusters with respect to data security measures. In this paper we proposed three models related to this networking model, which is CLOUD Networking Infrastructure, Connection Oriented model, Communication Oriented model. The detailed description of all three models are in the upcoming sections of this paper. Keywords: Cloud computing based computer networking model, A virtual model for computer networking, Computer Networking model based on virtualization, Virtualization based computer networking model.

Encryption Algorithm of Automatic Single Chip Computer and Embedded System Based on Big Data

With the rapid development of network technology and communication technology, modern society is rapidly transforming towards an information society based on big data. The single-chip microcomputer and embedded system have received widespread attention from all walks of life since they came out. Now it has fully penetrated into people’s daily work and life. In this regard, it is of great significance to study the encryption algorithms of automated single-chip microcomputers and embedded systems based on big data. The purpose of this article is to study the encryption algorithm of the automatic single-chip microcomputer and embedded system based on big data. This article adopts research methods such as literature data method, mathematical statistics method, and logical analysis method to study the encryption algorithm of automatic single-chip computer and embedded system based on big data. It mainly optimizes and adjusts the existing data encryption algorithm and strengthens the information. Through the normal test and abnormal test of the data encryption system, it can be seen that the system can encrypt and store data, which can be said to realize transparent encryption and decryption in the process of reading and writing. Studies have shown that although the system’s built-in function takes 5-6 seconds to open a file, the time it takes for the function to open a file is between 45-48 seconds, which takes a little longer. When reading the specified data from the specified file, the correct data can be read only by providing a consistent key. It can be seen that the system is equivalent to providing data security measures in two aspects: identity authentication and data encryption. This design scheme can effectively protect the data security of the system.

The Overview of Security Measures in Big Data

Big data refers to the huge volume of data that is being produced by different organizations after every second. It is hard to handle the such volume of data, therefore this data is combined and managed through the servers of organizations using complex algorithms. Data is currently the most important asset of any organization in every field with which many operations can be performed. Big Data exerts different properties those are volume, velocity, veracity, and variety. Now there are different threats regarding the security of big data. Organizations are using different methods to secure big data. Big data security measures include architecture security, infrastructure security, and data privacy. In this paper multiple security concerns are discussed and how they can help organizations secure their data.

ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare

Training sophisticated machine learning models usually requires many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in epsilon-differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. ESCAPED enables the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We have evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only present the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources.

Securing Input and Output Processes on The Web to Minimize SQL-Injection and XSS Attacks Using IDS and IPS Methods

Some of the gaps that exist in web applications are often encountered, such as input both in the input form and input in the url. One of the attacks that are often found in data input is SQL-injection and XSS. Therefore one of the precautions is to carry out data security measures in the input and output process. Here the author uses the IDS and IPS methods as security of input forms from SQL-injection and XSS attacks. IDS is used as a detection and recording of attacks while IPS functions as a blocking access to the website if SQL-injection and XSS attacks are detected. In this case filtering uses the preg_match () function where the writer inserts the word into preg_match () as a rule which later if the user inputs what is in the preg_match () rule then the user is trying to do an injection attack. The data retrieved by the IDS script are ip_address, injected files, scripts, browsers used. IPS uses ip_address as a rule to block access from users when doing injection. It is hoped that the IDS and IPS scripts created will help secure the input output process that is on the web in order to minimize the occurrence of SQL-injection and XSS attacks.

Invalidation of the EU–US Privacy Shield: impact on data protection and data security regarding the transfer of personal data to the United States

The Court of Justice of the European Union has attracted international attention with its ruling, in which it declared the EU–US Privacy Shield invalid. Many companies have a connection to the United States, whether through service providers or affiliated companies. Data exporters and data importers are still wondering whether and how they can manage the transfer of personal data to the United States in accordance with European data protection laws. The national German data protection authorities have already published general recommendations for action, but these did not provide any remedy either. In November 2020, the European Data Protection Board published recommendations on measures to complement the transfer tools to ensure compliance with the European level of protection of personal data. The following article presents these recommendations and focuses on data security measures that play a major role in the future transfer of personal data to the United States.

Legal Schemes For The Protection Of Personal Data In The Context Of Digitalization

The purpose of this article's research is to problematize the legal regulation of work with personal data and other confidential information in the context of large-scale digitization. We sought to systematize the basic concepts used in addressing the topic of information security, such as information, types of protected data, information leakage, types and channels of information leakage. Particular attention is paid to personal data security measures: the legal framework; the specifics of the appointment of sanctions; gaps and collisions in the interaction of legislative acts are determined.

Do data security measures, privacy regulations, and communication standards impact the interoperability of patient health information? A cross-country investigation

BackgroundThe lack of interoperability is one of the biggest obstacles to the complete digitalization of patient health information in electronic medical records (EMR). The high volume of data breaches has put pressure on care providers to adopt data protection measures to remain compliant with legal requirements. Extreme data protection measures can impede information flow, but they also instill confidence in secure information sharing. This study investigates how the adoption of security measures, privacy regulations, and communication standards has impacted patient health information interoperability at technical (TI), semantic (SI), and organizational (OI) levels within the hospitals. MethodsThe study utilizes a quasi-experimental research design to probe the relationships of interest. Secondary data from a survey of randomly selected 773 hospitals conducted by the European Commission in over 30 countries in Europe is used to understand the relationships. The study counters selection bias and accounts for systematic differences in adopting treatments of interest in the hospitals using the propensity score-based approaches for the observational data. ResultsThe empirical models that account for selection bias explain more observational data variations than those that did not. Access control measures on workstations are linked to 44 % lesser odds of experiencing TI problems. However, hospitals with regional and organizational level privacy regulations have 85 % and 76 % higher odds of experiencing SI and OI problems, respectively. On the other hand, hospitals with a single hospital-wide EMR are 53 % and 43 % less likely to experience TI and SI problems, respectively, in comparison to those with multiple EMR systems. ConclusionThe study highlights the differential impacts of data protection measures on the hospitals' three key types of interoperability problems (i.e., TI, SI, and OI). Homogenous EMR systems type and substantial investment in technology are critical to supporting health information interoperability within the hospitals. The study findings inform policy considerations for improving specific aspects of health information's interoperability while preserving patient data privacy and security.

Implementation and Application of Telemedicine in China: Cross-Sectional Study.

BackgroundTelemedicine has been used widely in China and has benefited a large number of patients, but little is known about the overall development of telemedicine.ObjectiveThe aim of this study was to perform a national survey to identify the overall implementation and application of telemedicine in Chinese tertiary hospitals and provide a scientific basis for the successful expansion of telemedicine in the future.MethodsThe method of probability proportionate to size sampling was adopted to collect data from 161 tertiary hospitals in 29 provinces, autonomous regions, and municipalities. Charts and statistical tests were applied to compare the development of telemedicine, including management, network, data storage, software and hardware equipment, and application of telemedicine. Ordinal logistic regression was used to analyze the relationship between these factors and telemedicine service effect.ResultsApproximately 93.8% (151/161) of the tertiary hospitals carried out telemedicine services in business-to-business mode. The most widely used type of telemedicine network was the virtual private network with a usage rate of 55.3% (89/161). Only a few tertiary hospitals did not establish data security and cybersecurity measures. Of the 161 hospitals that took part in the survey, 100 (62.1%) conducted remote videoconferencing supported by hardware instead of software. The top 5 telemedicine services implemented in the hospitals were teleconsultation, remote education, telediagnosis of medical images, tele-electrocardiography, and telepathology, with coverage rates of 86.3% (139/161), 57.1% (92/161), 49.7% (80/161), 37.9% (61/161), and 33.5% (54/161), respectively. The average annual service volume of teleconsultation reached 714 cases per hospital. Teleconsultation and telediagnosis were the core charging services. Multivariate analysis indicated that the adoption of direct-to-consumer mode (P=.003), support from scientific research funds (P=.01), charging for services (P<.001), number of medical professionals (P=.04), network type (P=.02), sharing data with other hospitals (P=.04), and expertise level (P=.03) were related to the effect of teleconsultation. Direct-to-consumer mode (P=.01), research funding (P=.01), charging for services (P=.01), establishment of professional management departments (P=.04), and 15 or more instances of remote education every month (P=.01) were found to significantly influence the effect of remote education.ConclusionsA variety of telemedicine services have been implemented in tertiary hospitals in China with a promising prospect, but the sustainability and further standardization of telemedicine in China are still far from accomplished.

Pharmacists' Perspectives on the Use of My Health Record.

(1) Background: My Health Record (MHR) is a relatively new nationwide Australian digital health record system accessible by patients and a range of healthcare professionals. Pharmacists will be key contributors and users of the MHR system, yet little is known about the perceived barriers and benefits of use. (2) Objective: To explore pharmacists’ perspectives related to potential benefits and barriers associated with use of MHR. (3) Methods: An online survey was developed and face-validated. The survey was advertised to Australian pharmacists on pharmacy professional bodies’ websites. This was a cross-sectional study using an anonymous questionnaire. Descriptive statistics were used to describe the distribution of the data. Chi-square, Kendall’s tau coefficient (tau-c) and Kruskal–Wallis tests were used to examine the relationships where appropriate. (4) Results: A total of 63 pharmacists completed the survey. The majority of respondents worked in a metropolitan area (74%), and the most common workplace setting was community pharmacy (65%). Perceived benefits identified by responders include that the use of MHR would help with continuity of care (90%), and that it would improve the safety (71%) and quality (75%) of care they provided. Importantly, more than half of pharmacists surveyed agreed that MHR could reduce medication errors during dispensing (57%) and could improve professional relationships with patients (57%) and general practitioners (59%). Potential barriers identified by pharmacists included patients’ concerns about privacy (81%), pharmacists’ own concern about privacy (46%), lack of training, access to and confidence in using the system. Sixty six percent of respondents had concerns about the accuracy of information contained within MHR, particularly among hospital and general practice pharmacists (p = 0.016) and almost half (44%) had concerns about the security of information in the system, mainly pharmacists working at general practice and providing medication review services (p = 0.007). Overall satisfaction with MHR varied, with 48% satisfied, 33% neither satisfied nor dissatisfied, and 19% dissatisfied, with a higher satisfaction rate among younger pharmacists (p = 0.032). (5) Conclusions: Pharmacists considered that the MHR offered key potential benefits, notably improving the safety and quality of care provided. To optimize the use of MHR, there is a need to improve privacy and data security measures, and to ensure adequate provision of user support and education surrounding the ability to integrate use of MHR with existing workflows and software.

Abstract IA04: Data-driven recruitment: Implementation of digital marketing strategies for improved participant response rates and retention

Abstract Background: Decline in epidemiology cohort response rates has been documented over decades, with precipitous declines in recent years. Incentives and repeated contact attempts have been shown to increase retention to varying degrees, but the effects of the different types of incentives and methods of contact—details necessary for reliable replication—remain poorly understood. Numerous factors related to study implementation and participant characteristics likely affect response rates, but few cohort studies have published the detailed empirical data that would be necessary to assess their effect. The small number of cohort studies that have published analyses of their recruitment protocols raises the question of how many studies are systematically and rigorously examining their methods. A 2011 review suggested that cohort studies may be less likely to engage in these activities due to concerns of risk to participant loyalty and the cost of assessment. Epidemiology cohorts often involve many concurrent activities and overlapping projects, which may also contribute to the complexity of the task. Considering the long duration of cohort studies, some may perceive that study staff can accurately intuit best practices. However, these challenges are not unique to epidemiology cohorts. The private sector is also concerned with attracting and retaining customers, and as such, companies have developed tools that enable rapid, low-cost reporting on their methods. In the field of marketing, customer relationship management (CRM) systems facilitate both the desired outcome (a customer buying a product) and the collection of data to identify factors that influenced the outcome. Aspects of these targeted marketing approaches and their related software tools provide both a model and a means by which epidemiology cohorts can better understand the constellation of factors that affect attrition among their own populations. Methods: The California Teachers Study (CTS) is a large prospective cancer epidemiology cohort that began in 1995. Since 2013, the CTS has used a commercially available CRM platform, modified with a variety of native applications (apps) and integrations, to manage participant recruitment and data collection for multiple projects. To manage a large-scale biobanking project and multiple tissue collection projects, we began with the platform’s standard contact data management and activity tracking features. We added an app for geolocation fieldwork management and a custom mobile app for surveys and biospecimen chain-of-custody tracking. To manage two follow-up questionnaires, we used available integrations for survey development, email marketing automation, a cloud-based call center with automated activity logging, and a web form for participants to submit information securely from our study website. Central features of all projects have included native reports and dashboards, workflow automations that reduce data entry, and data security measures. Results: The CRM platform has scaled to meet the needs of multiple concurrent projects with highly varied study designs and staffing requirements. By conducting our recruitment activities within the CRM, each interaction with the system (an incoming call from a participant, a blood collection, an automated survey reminder) passively generates useful metadata. These metadata, in concert with structured and unstructured data from participants and staff, enable timely monitoring and retrospective analysis using native reporting tools within the system. The ability to view and analyze clean data from the outset of each project revealed key—and sometimes unexpected—factors that were influencing response. Analysis of data across projects has revealed that characteristics of participants, staff, protocol decisions, technology, and equipment all influence outcomes in distinct ways. Conclusion: Existing commercially available tools, such as a CRM system, can be readily configured to meet the specific research, process, and security requirements of population sciences research. In the CTS, using a CRM system helped reveal that different factors affected participation differently, even within the same project. This presentation will discuss these factors, the approaches studies can use to collect data about these factors, and lessons learned about ways to further improve response rates. Broader adoption of CRM tools could enable the type of data collection and analysis necessary to more precisely define the factors that affect participation and develop more targeted and successful recruitment methods. Citation Format: Jennifer Benbow. Data-driven recruitment: Implementation of digital marketing strategies for improved participant response rates and retention [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA04.

Integrating a Secure and Generic Mobile App for Patient Reported Outcome Acquisition into an EHR Infrastructure Based on FHIR Resources.

Both patients and physicians of the University Hospital of Erlangen would welcome means of digital data exchange between patients and the hospital. This work presents a system's architecture, FHIR based data model, security measures, and application flow based on patient's smartphones and a public cloud infrastructure to obtain patient reported outcome measures and merge that information with clinical routine data.

A CP-ABE Algorithm Based HDFS Distributed Storage Data Security Solution

With the continuous development of technology, traditional data encryption technology has been cracked, existing data security measures can no longer meet the user’s data security needs, and the problem of data security remains the primary issue facing cloud storage. Based on the analysis of cloud storage data security, this paper proposes a CP-ABE-based HDFS distributed storage data security solution to the problems of traditional attribute-based data encryption technology (CP-ABE). After analysis of the algorithm, the algorithm can ensure the security of the data stored on the cloud platform users through the attribute-based encryption algorithm.

Privacy and Security Issues with Mobile Health Research Applications

This article examines the privacy and security issues associated with mobile application-mediated health research, concentrating in particular on research conducted or participated in by independent scientists, citizen scientists, and patient researchers. Building on other articles in this issue that examine state research laws and state data protection laws as possible sources of privacy and security protections for mobile research participants, this article focuses on the lack of application of federal standards to mobile application-mediated health research. As discussed in more detail below, the voluminous and diverse data collected by some independent scientists who use mobile applications to conduct health research may be at risk for unregulated privacy and security breaches, leading to dignitary, psychological, and economic harms for which participants have few legally enforceable rights or remedies under current federal law. Federal lawmakers may wish to consider enacting new legislation that would require otherwise unregulated health data holders to implement reasonable data privacy, security, and breach notification measures.

Rationalizing Urban Transportation using Smart Card Data

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

Design and accrual of S1417CD: Development of a prospective financial impact assessment tool in patients with metastatic colorectal cancer (mCRC).

TPS6652 Background: Few studies have assessed the financial impact of cancer diagnosis (dx) in diverse patients (pts) and caregivers (cgs) using objective and standard financial measures. S1417CD, led by the SWOG Cancer Research Network, is the first prospective cohort study assessing financial outcomes to be conducted in the NCI Community Oncology Research Program (NCORP). We present our experience with design and accrual. Methods: Pts age ≥ 18 within 120 days of mCRC dx were considered eligible and asked to identify a caregiver (cg) who could participate concurrently. The primary endpoint is incidence of treatment-related financial hardship, defined as ≥ 1 of the following: debt accrual, selling/refinancing home, ≥ 20% income decline, or borrowing money. Measures include 1) pt and cg surveys (baseline (BL), 3, 6, 9 and 12 months (mo)) assessing out-of-pocket spending, financial impacts, cg burden, and quality of life and 2) pt credit reports (BL, 6, and 12 mo). Linkage to records from TransUnion, a national credit agency, required pt social security number (SSN) and processes for batched credit report transfer via secure web portal. The accrual goal was n = 374 pts in 3 years. The study activated on Apr 1, 2016 and closed on Feb 1, 2019 after reaching its accrual goal. A total of 380 pts (median age 59.7 years) and 155 cgs enrolled (41% cg participation). Enrollment steadily increased during the study period; 56% enrolled in the last 12 mo. Credit data were not obtainable for 76 (20%) pts due to early death, lack of credit, or inability to match records. S1417CD, the first cooperative group led study assessing financial outcomes in the community setting, completed enrollment faster than anticipated. Required SSN collection was not a barrier to enrollment, which improved as sites became familiar with data security measures. Robust accrual to S1417CD demonstrates pts’ and cgs’ desire to improve understanding of financial toxicity and its solutions. Follow-up will conclude in 12 mo with results to follow. SWOG plans to launch a randomized study (S1912) assessing the impact of financial navigation on household finances, using credit data for primary endpoint assessment. Clinical Trials Registry Identifier NCI-2015-01885. Clinical trial information: NCT02728804.

Obowiązki organów administracji publicznej w zakresie przetwarzania danych w systemach zinformatyzowanych przed i po wejściu w życie RODO i nowelizacji ustawy o ochronie danych osobowych

Celem artykułu jest wskazanie konsekwencji prawnych w zakresie dotyczącym zapewnienia bezpieczeństwa przetwarzania danych osobowych w systemach zinformatyzowanych. Ustawodawca wyznaczył administratorom danych osobowych, konkretne obowiązki w kwestii ustalenia odpowiednich środków organizacyjnych, osobowych, rzeczowych i technicznych, które mają uniemożliwić posługiwanie się danymi osobowymi przez nieuprawnione podmioty w nielegalnym celu. Od maja 2018 r. obowiązuje Rozporządzenie Parlamentu Europejskiego i Rady 2016/679 o ochronie danych osobowych. Wprowadza nowe regulacje prawne np. ustanawia inspektora ochrony danych. Pierwsza część artykułu wskazuje zadania administratorów danych polegające na użyciu środków zabezpieczających dane osobowe w systemach zinformatyzowanych, przed wejściem w życie RODO i ustawy z 10.5.2018 r. o ochronie danych osobowych. W drugiej zawarto najważniejsze zmiany odnoszące się do administratorów danychosobowych, określone w RODO i ustawie o ochronie danych osobowych. Metody badawcze obejmują komparatystyczną analizę podstawowych tekstów aktów prawnych z wykorzystaniem literatury przedmiotu (zastosowano metodę porównawczo-prawną).

Thought Leader Perspectives on Participant Protections in Precision Medicine Research.

Precision medicine research is rapidly taking a lead role in the pursuit of new ways to improve health and prevent disease, but also presents new challenges for protecting human subjects. The extent to which the current “web” of legal protections, including technical data security measures, as well as measures to restrict access or prevent misuse of research data, will protect participants in this context remains largely unknown. Understanding the strength, usefulness, and limitations of this constellation of laws, regulations, and procedures is critical to ensuring not only that participants are protected, but also that their participation decisions are accurately informed. To address these gaps, we conducted in-depth interviews with a diverse group of 60 thought-leaders to explore their perspectives on the protections associated with precision medicine research.

Hospital Readmissions Among People Experiencing Homelessness: A Cohort Study of Linked Hospitalisation and Mortality Data in England for 3,222 Homeless Inpatients

Background: Homeless hospital inpatients are often discharged to unstable accommodation or the street, which may increase the risk of readmission. Methods: We conducted a cohort study of 3,222 homeless patients discharged alive from hospitals across England between 2013 and 2016, with mean 2.4 years of follow-up. A comparison group of housed patients living in deprived areas was matched 1:1 on age group, sex, hospital and year of discharge. Counts of emergency re-admissions, planned re-admissions and A&E visits post-discharge were derived from national hospital databases. We reported readmission risk and used negative binomial regression to estimate rate ratios. Findings: After adjusting for health at study baseline, homeless patients had 3.77 (95% CI 3.46-4.10) times the rate of emergency readmission, 0.71 (0.63-0.81) times the rate of planned readmission, and 3.76 (3.53-4.01) times the rate of AE p<0.001) times the rate of AE 0.73-1.06; p=0.140) or emergency re-admissions (1.08; 0.98-1.18; p=0.102). Interpretation: Homeless inpatients have high rates of emergency readmission use that are not explained by their health. This highlights the need for discharge arrangements that address health, housing and social care needs. Funding Statement: This study was supported by the National Institute for Health Research (NIHR) [Project number: 13/156/10 to HS & DR]. The authors also acknowledge the support from the Health Data Research (HDR) UK which receives its funding from HDR UK Ltd funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. ACH’s salary is provided by Central and North West London NHS Community Trust. AS is funded by UCLH Foundation Trust. DL is funded by the NIHR [DRF-2018-11-ST2-016]. JN is part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King's College London. RBl is supported by a UK Research and Innovation Fellowship funded by a grant from the Medical Research Council [MR/S003797/1]. SL is funded by NIHR [ICA-CDRF-2016-02-042]. RBy is supported the NIHR Applied Research Collaboration (ARC) South West Peninsula.. Declaration of Interests: NH is medical director, and ACH is a trustee of the Pathway: Healthcare for homeless people charity. AS is Clinical Lead and Manager for Find and Treat. All other authors declare no conflicts of interest. Ethics Approval Statement: Collection of patient identifiers and data linkage were performed without explicit consent from participants due to the complexities in retrospectively identifying and obtaining consent, and because we wanted to use existing secondary data to examine mortality in this group. We engaged with people who had experience of homelessness and most felt that studies such as this are acceptable without consent, providing adequate data security measures are in place and studies have regulatory and ethical approval. This research was undertaken following approval (reference 16/CAG/0021) from the Secretary of State for Health through the Confidentiality Advisory Group (CAG). The Health Research Authority Research Ethics Committee approved the study (REC 16/EE/0018). In addition, local R&D approvals were obtained prior to local data collection at each of the Homeless Hospital Discharge Fund sites. After data linkage, we destroyed personal identifying data and undertook all analyses using the anonymised dataset outlined previously and in Figure 1. All study data were stored on the UCL Data Safe Haven, which has been certified to the ISO:27001:2013 information security standard and conforms to the NHS Information Governance Toolkit.