File Name: data security and privacy .zip
- What is Cyber Security?
- International Cybersecurity and Data Privacy Outlook and Review – 2021
- International Cybersecurity and Data Privacy Outlook and Review – 2021
Department of Commerce regarding the collection, use, and retention of personal information transferred from the European Economic Area, the UK, and Switzerland to the United States.
What is Cyber Security?
Metrics details. Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare.
Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed.
To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed the state-of-the-art security and privacy challenges in big data as applied to healthcare industry, assessed how security and privacy issues occur in case of big healthcare data and discussed ways in which they may be addressed.
We mainly focused on the recently proposed methods based on anonymization and encryption, compared their strengths and limitations, and envisioned future research directions. Change is the new norm for the global healthcare sector. In fact, digitization of health and patient data is undergoing a dramatic and fundamental shift in the clinical, operating and business models and generally in the world of economy for the foreseeable future.
This shift is being spurred by aging populations and lifestyle changes; the proliferation of software applications and mobile devices; innovative treatments; heightened focus on care quality and value; and evidence-based medicine as opposed to subjective clinical decisions—all of which are leading to offer significant opportunities for supporting clinical decision, improving healthcare delivery, management and policy making, surveilling disease, monitoring adverse events, and optimizing treatment for diseases affecting multiple organ systems [ 1 , 2 ].
As noted above, big data analytics in healthcare carries many benefits, promises and presents great potential for transforming healthcare, yet it raises manifold barriers and challenges. Indeed, the concerns over the big healthcare data security and privacy are increased year-by-year. Additionally, healthcare organizations found that a reactive, bottom-up, technology-centric approach to determining security and privacy requirements is not adequate to protect the organization and its patients [ 3 ].
Motivated thus, new information systems and approaches are needed to prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data. In this paper, we discuss some interesting related works and present risks to the big health data security as well as some newer technologies to redress these risks. Thereafter, we provide some proposed techniques and approaches that were reported in the literature to deal with security and privacy risks in healthcare while identifying their limitations.
Lastly, we offer conclusions and highlight the future directions. Seamless integration of greatly diverse big healthcare data technologies can not only enable us to gain deeper insights into the clinical and organizational processes but also facilitate faster and safer throughput of patients and create greater efficiencies and help improve patient flow, safety, quality of care and the overall patient experience no matter how costly it is.
Such was the case with South Tyneside NHS Foundation Trust, a provider of acute and community health services in northeast England that understands the importance of providing high quality, safe and compassionate care for the patients at all times, but needs a better understanding of how its hospitals operate to improve resource allocation and wait times and to ensure that any issues are identified early and acted upon [ 4 ].
Another example is the UNC Health Care UNCHC , which is a non-profit integrated healthcare system in North Carolina that has implemented a new system allowing clinicians to rapidly access and analyze unstructured patient data using natural-language processing. In fact, UNCHC has accessed and analyzed huge quantities of unstructured content contained in patient medical records to extract insights and predictors of readmission risk for timely intervention, providing safer care for high-risk patients and reducing re-admissions [ 5 ].
Moreover in the United States, the Indiana Health Information Exchange, which is a non-profit organization, provides a secure and robust technology network of health information linking more than 90 hospitals, community health clinics, rehabilitation centers and other healthcare providers in Indiana.
It allows medical information to follow the patient hosted in one doctor office or only in a hospital system [ 6 ]. One more example is Kaiser Permanente medical network based in California. It has more than 9 million members, estimated to manage large volumes of data ranging from Big data analytics is used also in Canada, e.
This hospital succeeded to improve the outcomes for newborns prone to serious hospital infections. Another example is the Artemis project, which is a newborns monitoring platform designed mercy to a collaboration between IBM and the Institute of Technology of Ontario. In Europe and exactly in Italy, the Italian medicines agency collects and analyzes a large amount of clinical data concerning expensive new medicines as part of a national profitability program.
Based on the results, it may reassess the medicines prices and market access terms [ 9 ]. Launched in , in Costa Rica that has been officially selected as the first country, the initiative is working on an mCessation for tobacco program for smoking prevention and helping smokers quit, an mCervical cancer program in Zambia and has plans to roll out mHypertension and mWellness programs in other countries.
As new users of SOPHIA, they become part of a larger network of hospitals in 46 countries that share clinical insights across patient cases and patient populations, which feeds a knowledge-base of biomedical findings to accelerate diagnostics and care [ 12 ]. While the automations have led to improve patient care workflow and reduce costs, it is also rising healthcare data to increase probability of security and privacy breaches. Additionally, ransomware, defined as a type of malware that encrypts data and holds it hostage until a ransom demand is met, has identified as the most prominent threat to hospitals.
Additional findings of this report include:. These findings point to a pressing need for providers to take a much more proactive and comprehensive approach to protecting their information assets and combating the growing threat that cyber attacks present to healthcare. Several prosperous initiatives have appeared to help the healthcare industry continually improve its ability to protect patient information.
The review brought concrete recommendations to maximize benefits and minimize risks of big data [ 14 , 15 ], namely:. Policy attention should focus more on the actual uses of big data and less on its collection and analysis. Such existing policies are unlikely to yield effective strategies for improving privacy, or to be scalable over time.
Policy concerning privacy protection should be addressing the purpose rather than prescribing the mechanism. Research is needed in the technologies that help to protect privacy, in the social mechanisms that influence privacy preserving behavior, and in the legal options that are robust to changes in technology and create appropriate balance among economic opportunity, national priorities, and privacy protection.
Increased education and training opportunities concerning privacy protection, including career paths for professionals. Programs that provide education leading to privacy expertise are essential and need encouragement. Privacy protections should be extended to non-US citizens as privacy is a worldwide value that should be reflected in how the federal government handles personally identifiable information from non-US citizens [ 16 ].
Security and privacy in big data are important issues. Privacy is often defined as having the ability to protect sensitive information about personally identifiable health care information.
While security is typically defined as the protection against unauthorized access, with some including explicit mention of integrity and availability. It focuses on protecting data from pernicious attacks and stealing data for profit. While healthcare organizations store, maintain and transmit huge amounts of data to support the delivery of efficient and proper care, the downsides are the lack of technical support and minimal security.
Complicating matters, the healthcare industry continues to be one of the most susceptible to publicly disclosed data breaches. In fact, attackers can use data mining methods and procedures to find out sensitive data and release it to the public and thus data breach happens. Whereas implementing security measures remains a complex process, the stakes are continually raised as the ways to defeat security controls become more sophisticated.
Accordingly, it is critical that organizations implement healthcare data security solutions that will protect important assets while also satisfying healthcare compliance mandates. In terms of security and privacy perspective, Kim et al.
In this regards, healthcare organizations must implement security measures and approaches to protect their big data, associated hardware and software, and both clinical and administrative information from internal and external risks. Yazan et al. This model is designed to address the phases of the big data lifecycle and correlate threats and attacks that face big data environment within these phases, while [ 21 ] address big data lifecycle from user role perspective: data provider, data collector, data miner, and decision maker.
The model proposed in [ 20 ] comprised of four interconnecting phases: data collection phase, data storage phase, data processing and analysis, and knowledge creation.
Furthermore, CCW The Chronic Conditions Data Warehouse follows a formal information security lifecycle model, which consists of four core phases that serve to identify, assess, protect and monitor against patient data security threats.
This lifecycle model is continually being improved with emphasis on constant attention and continual monitoring [ 21 ]. In this paper, we suggest a model that combines the phases presented in [ 20 ] and phases mentioned in [ 21 ], in order to provide encompass policies and mechanisms that ensure addressing threats and attacks in each step of big data life cycle. Data collection phase This is the obvious first step.
It involves collecting data from different sources in various formats. From a security perspective, securing big health data technology is a necessary requirement from the first phase of the lifecycle. Therefore, it is important to gather data from trusted sources, preserve patient privacy there must be no attempt to identify the individual patients in the database and make sure that this phase is secured and protected.
Indeed, some mature security measures must be used to ensure that all data and information systems are protected from unauthorized access, disclosure, modification, duplication, diversion, destruction, loss, misuse or theft.
Data transformation phase Once the data is available, the first step is to filter and classify the data based on their structure and do any necessary transformations in order to perform meaningful analysis. More broadly, data filtering, enrichment and transformation are needed to improve the quality of the data ahead of analytics or modeling phase and remove or appropriately deal with noise, outliers, missing values, duplicate data instances, etc. On the other side, the collected data may contain sensitive information, which makes extremely important to take sufficient precautions during data transformation and storing.
In order to guarantee the safety of the collected data, the data should remain isolated and protected by maintaining access-level security and access control utilizing an extensive list of directories and databases as a central repository for user credentials, application logon templates, password policies and client settings [ 22 ], and defining some security measures like data anonymization approach, permutation, and data partitioning.
Data modeling phase Once the data has been collected, transformed and stored in secured storage solutions, the data processing analysis is performed to generate useful knowledge. In this phase, supervised data mining techniques such as clustering, classification, and association can be employed for feature selection and predictive modeling.
Further, there also exist several ensembles of learning techniques that improve accuracy and robustness of the final model. On the other side, it is crucial to provide secure processing environment. In fact, the focus of data miners in this phase is to use powerful data mining algorithms that can extract sensitive data.
Therefore, the process of data mining and the network components in general, must be configured and protected against data mining based attacks and any security breach that may happen, as well as make sure that only authorized staff work in this phase. This process helps eliminate some vulnerabilities and mitigates others to a lower risk level.
Knowledge creation phase Finally, the modeling phase comes up with new information and valued knowledges to be used by decision makers. These created knowledges are considered sensitive data, especially in a competitive environment. Indeed, healthcare organizations aware of their sensitive data e. Accordingly, security compliance and verification are a primary objective in this phase.
At all stages of big data lifecycle, it requires data storage, data integrity and data access control. Various technologies are in use to ensure security and privacy of big healthcare data. Most widely used technologies are:. It serves vital functions within any organization: securing access to corporate networks, protecting the identities of users, and ensuring that the user is really who he is pretending to be.
The information authentication can pose special problems, especially man-in-the-middle MITM attacks. Most cryptographic protocols include some form of endpoint authentication specifically to prevent MITM attacks. For instance [ 23 ], transport layer security TLS and its predecessor, secure sockets layer SSL , are cryptographic protocols that provide security for communications over networks such as the Internet.
Several versions of the protocols are in widespread use in applications like web browsing, electronic mail, Internet faxing, instant messaging and voice-over-IP VoIP.
This algorithm has been used to make sure data security and manage relations between original data and replicated data. It is also allowed only to an authorized person to read or write critical data. Paper [ 25 ] proposes a novel and simple authentication model using one time pad algorithm. It provides removing the communication of passwords between the servers. In a healthcare system, both healthcare information offered by providers and identities of consumers should be verified at the entry of every access.
Its solutions protect and maintain ownership of data throughout its lifecycle—from the data center to the endpoint including mobile devices used by physicians, clinicians, and administrators and into the cloud.
Encryption is useful to avoid exposure to breaches such as packet sniffing and theft of storage devices. Healthcare organizations or providers must ensure that encryption scheme is efficient, easy to use by both patients and healthcare professionals, and easily extensible to include new electronic health records.
International Cybersecurity and Data Privacy Outlook and Review – 2021
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Deploying existing data security solutions to the Internet of Things IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. View via Publisher. Save to Library. Create Alert.
Metrics details. Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare. Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed.
This Data Security and Privacy Toolkit aims to educate real estate associations, brokers, agents, and multiple listing services about the need for data security and privacy and assist them in complying with legal responsibilities. The Toolkit provides:. Skip to main content. Your resource for all things Real Estate. View All.
International Cybersecurity and Data Privacy Outlook and Review – 2021
Cyber security is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It's also known as information technology security or electronic information security. The term applies in a variety of contexts, from business to mobile computing, and can be divided into a few common categories. A compromised application could provide access to the data its designed to protect. Successful security begins in the design stage, well before a program or device is deployed.
Click for PDF. In addition to the EU, different legal developments occurred in other jurisdictions around the globe, including in other European jurisdictions, the Asia-Pacific region, the Middle East, Africa and Latin America. International Data Transfers. The Schrems II Ruling 2.
This book provides an overview of the most recent developments in Internet of Things IoT security and data protection. It presents the results of several international research projects addressing this topic from complementary angles. They discuss the most recent approach to support trusted IoT, including new models of privacy risk assessment, labeling and certification, and contractual tools such as Privacy PACT. Practical implementations, such as in the European Large Scale Pilots on IoT for Smart Cities Synchronicity , are presented, explaining how they address security, privacy and data protection. Finally, innovative models to secure IoT systems are presented for the network and end-nodes security, including network threats analysis.
The Privacy Vanguard Award honors those who have scaled new heights in privacy. Who comes to mind? Nominate them by March
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It seems that you're in Germany. We have a dedicated site for Germany. The vision of ubiquitous computing and ambient intelligence describes a world of technology which is present anywhere, anytime in the form of smart, sensible devices that communicate with each other and provide personalized services.
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Беккер отшвырнул пистолет и без сил опустился на ступеньку. Впервые за целую вечность он почувствовал, что глаза его застилают слезы, и зажмурился, прогоняя влажную пелену. Он знал, что для эмоций еще будет время, а теперь пора отправляться домой. Он попробовал встать, но настолько выбился из сил, что не смог ступить ни шагу и долго сидел, изможденный вконец, на каменных ступеньках, рассеянно разглядывая распростертое у его ног тело.
Беккер как завороженный смотрел на человека, входящего в туалетную комнату. Он показался ему смутно знакомым.