The mass digitization of medical data expanded the possibility of improving healthcare through the application of big data analytics. However, personal medical issues are considered private matters and as a result, the use of patient data is highly regulated by privacy laws such as HIPAA and HITECH. On top of that, any data with value is a target for criminals, and thus, healthcare data must be kept secure. While striving to meet security and privacy challenges, the medical community is trying to get the most out of its valuable data.
Technical capabilities in healthcare also led to an increased focus and evidence-based decisions. Health care researchers and professionals are seeing data as the key to improving care, informing clinical decisions, tracking disease, and monitoring adverse effects of drugs or medical devices.
None of these improvements are possible if healthcare data cannot be shared or operationalized without ensuring both security and privacy. This means that leveraging big data requires systems that not only unlock new insights, but also protect the privacy of patients.
Since threats to privacy and security keep evolving, stakeholders must also actively refine their protective methods. With the COVID-19 pandemic leading to a pronounced reliance on digital technology, hackers leveraged cyber crime opportunities According to a report from Critical Insights, data breaches reached an all-time high in 2021, exposing a record amount of sensitive data.
In trying to combat the threat of cyberattacks, organizations have been finding that relying on a bottom-up, reactive, and technically focused protection strategy is not enough to address big data security and privacy issues in healthcare. Instead, experts are recommending a proactive, top-down approach that includes proper training of employees and other non-technical methods.
The Differences Between Big Data Security and Privacy
Security and privacy may seem like very similar concepts, but in the context of healthcare data, there are important distinctions between them.
- Security of healthcare data. Healthcare security measures are designed to prevent unauthorized access, data theft and cyberattacks that could expose data.
- Privacy of healthcare data. Privacy measures are designed to prevent connections between personal medical information and specific individuals. While security measures may be focused on shielding data from intentional attacks and theft, privacy measures are focused on the ways for data to be handled and used safely. Privacy measures outline the ways in which patient data can be collected, transferred, and used with respect to both privacy regulations and ethical behavior.
The distinctions between these two concepts are particularly relevant when trying to address big data security and privacy in healthcare. Security measures must be designed to ensure the integrity and confidentiality of data. Measures like firewalls and encryption prevent data from corruption and unauthorized access. In some ways, security measures for protecting healthcare data also support privacy. Administrative structures and techniques like anonymization are designed to prevent organizations that handle patient data from using that data against patients’ wishes.
It is important to note that a patient can waive some degree of privacy by giving consent to an individual or organization. For instance, a patient could authorize their provider to share the results of a medical test with clinical researchers. If you’re interested in learning more about what disclosures are permitted for personal health information, check out this Ultimate Guide to Healthcare Data Security.
Securing the Entire Data Lifecycle
Companies that handle healthcare data must use security methods that protect both their assets and satisfy compliance concerns. Experts recommend that organizations consider the entire lifecycle of the data when applying security measures. The typical life cycle of healthcare data contains four phases: collection, storage, processing, and knowledge creation.
Data collection can involve gathering data in various formats from multiple sources. From a security standpoint, this should mean collecting data from reliable sources in a secure manner. Importantly, healthcare data may not come directly from patients, and companies receiving healthcare data must have systems in place to ensure their data collaboration is secure. Security measures for this part of the data lifecycle should prevent improper access, corruption, unauthorized disclosure, duplication, erasure, misuse, loss, and theft.
The first step of the storage phase involves filtering and characterizing the data according to predefined qualities. Some data may require preprocessing to facilitate future analysis. Preprocess steps like removing duplicate data or statistical noise are meant to improve the quality of collected data prior to any processing. This step could involve some security-related preprocessing, such as anonymization methods or data partitioning. The secure storage of data typically involves keeping it isolated and applying access control measures.
After data has been collected, preprocessed and stored securely, it is ready for the analysis phase. This stage involves the use of robust data mining techniques to generate useful knowledge and insights. The data mining process should be configured in a way that prevents mining-based attacks or breaches of this part of an organization’s network. Access control measures should also be in place to ensure that only authorized personnel can access data analysis processes.
The ideal result of a processing phase is the creation of valuable insights. These insights themselves are also regarded as valuable data that must be protected, just as the data used to create these insights must be protected by security measures.
The entire life cycle of big data in healthcare requires the ability to securely store and maintain integrity via access control. Securing the entire lifecycle becomes more complicated as more touchpoints are added by different organizations. Data providers, collectors, analyzers, and any other stakeholders must all play their responsible part in keeping healthcare data secure. Some collaborations use business associate agreements (BAAs) to hold parties accountable for unauthorized use, but these agreements only establish a reactive mechanism for addressing security malpractice.
Privacy Issues with Big Data in Healthcare
Any discussion about maintaining patient privacy in the United States must include the Health Insurance Portability and Accountability Act (HIPAA). Enacted into U.S. law in 1996, HIPPA established national standards for ensuring patient privacy. In Europe, the General Data Protection Regulation (GDPR) has established a strict standard for ensuring patient privacy.
As you are well aware, organizations that handle healthcare data should be using HIPAA-compliant software and IT solutions. Any systems or applications developed by a company must prioritize privacy, compliance, and any privacy agreements. When there is significant overlapping privacy protection provided by technical security measures, companies should use anonymization techniques, which aim to remove any identifying information that could be traced back to a specific individual. However, removing potentially identifying information from patient records can result in a significant loss of value. For example, a cancer diagnosis for a female patient in a certain hospital could be traced back to identify a specific person but removing that diagnosis from the record results in a loss of value for cancer research purposes. Other anonymization efforts add statistical noise to data to obfuscate any attempts at identification, but the addition of noise too can diminish the value of the original dataset. There are other approaches to big data security and protection of privacy in healthcare, until now, all have had disadvantages along with their advantages.
Addressing Big Data Security and Privacy Issues in Healthcare with TripleBlind
Ensuring the security and privacy of big data in healthcare is a complicated undertaking, and one that gets even more complicated as more entities get involved. However, for organizations in healthcare, not making use of big data simply isn’t an option anymore.
The innovative TripleBlind Solution is designed to simplify both the security and privacy of big data analytics for data collaborations. Our privacy-enhancing approach allows data collaborators to protect both valuable data and algorithms used to process that data, avoiding the need for addressing security concerns with BAAs. TripleBlind’s innovations build on well understood principles, such as federated learning and multi-party compute. Our innovations radically improve the practical use of privacy preserving technology, by adding true scalability and faster processing, with support for all data and algorithm types, including such as medical imaging or genomic data.
In addition to preserving security and privacy, our one-way encryption approach helps to retain a high level of data utility, unlike anonymization techniques.