Encryption is when information gets converted into secret codes that eventually hide the actual meaning of the data or file. The art of encryption and decryption is popularly known as cryptography. Encryption is the most secure and preferred communication method, which is commonly used by chatting and various applications where communication takes place. But how do calls get encrypted? And what is the importance of encryption in healthcare today?
Your most sensitive information is protected and secured through encryption.
In the past, the method of encryption was mostly used by military personnel and governments. Nowadays, encryption is used to preserve data stored on computers and storage devices along with data over other networks.
Machine learning has been undergoing rapid changes from the time of its evolution. Routine and daily tasks that the machines perform make the work of an individual easier but machine learning often gets avoided as there are major issues attached to it, such as data theft.
The models in machine learning can be substantial and strenuous, which can get shared across different users.
This sharing of models with different users cannot always be done as high computation is needed at every end.
To answer and solve this problem, cloud computing made it possible to hold the machine learning models with the help of cloud API’s and to get the desirable outcomes. Though cloud computing weakens computational power efficiency, it reveals your data to the various cloud providers.
Homomorphic encryption allows users to operate on ciphertexts to provide the desired outcomes or expected results to the users without harming the users’ sensitive data.
Like working on plain texts, operating on cipher-texts works through homomorphic encryption, which results in providing desired results and does not diverge from the expected results.
The encryption which gets offered in Homomorphic are basically categorized into the following:
- Encrypted = Encrypt (pub_key,plaintext)
- Decrypted = decrypt (priv_key, encrypted)
- Encrypted = eval (eval_key,f,encrypted)
- Pub_key, eval_key, priv_key = keygen ()
These might appear similar to asymmetric cryptography though the operations and function included in the third point function “f” in encryption and rebounds the encrypted value, which results in secure computation.
To maintain your conversations’ privacy, an app or a company must focus on using end-to-end encryption for calls, messages, video, or any other content.
This will make sure that :
- Only individuals who receive this message will know what’s been said or sent.
- Nobody other than the sender and receiver knows what’s the conversation all about.
End-to-end encryption is the most preferred method of security that secures the data of communication. It gets mandated automatically in the apps so that users don’t have to worry about switching it on and off.
How does it work?
As we discussed above, encryption simply covers up data with that of code, and then to decode it, a key is required.
End-to-End encryption for any app means that the data of a call, whether its audio or video, gets encrypted from your device and the device of the one with whom you are having a conversation.
The encrypted audio and video can only get decoded with the secret key shared in the same conversation.
The key functions as:
- A number that gets generated on the device that you called. It prevails on the same devices where the conversation takes place.
- The key does not gets shared on Google, other devices, and anyone else.
- The key automatically disappears when the phone ends.
Even if somebody gets access to your conversation or data, it can’t get decoded as the user does not have the key to decode it.
How does the Healthcare industry get affected?
Every organization and industry needs a secure way of communication to maintain the firm’s legitimacy and secrets.
Majorly Health-tech organizations cope with receiving raw data of patients to build robust and other healthcare products based on machine learning.
Despite the successful furnishing of machine learning on encrypted data.
Tech firms can develop and offer a range of products that can accelerate the development of medicines, enhance medical facilities, and facilitates them to harmonize various Electronic health records (EHR).
This EHR method allows the health tech companies to sleek their workflow and provide the safety and security of patient’s data.
This positive brunt in the healthcare sector and the privacy which gets provided will grant organizations that are building facial recognition technologies to provide more secure methods.
The governments and firms worldwide generally face criticism for implementing technologies without work plans of confidentiality and privacy.
Encrypted data, along with machine learning, will allow the healthcare industry to preserve the data of various customers and users for providing tech-based solutions.
Security Requirements in Healthcare
According to research conducted by Stanford University. 48% growth in medical data is expected this year.
This incorporates private and sensitive information of patients, health status, and insurance that revolves around hospitals’ doctor’s offices and desks.
Such sensitive and crucial information requires high levels of security for the patient’s data.
For such protection and prevention of data, whether it is on call, MRI, or digital recordings, the patients need to be encrypted.
The HIPAA security rule and guidelines were specially enacted to safeguard patients’ personal health information in an electronic manner that was created, received, used, and maintained by the companies.
The company covers all the healthcare providers, healthcare plans, healthcare clearinghouses, medicare prescription drugs, and their sponsors.
The protection provided under the security rule includes various categories such as:
- Administrative: Allocating data security responsibility and implementation of security training to all the employees of the organization.
- Physical: It protects electronic systems and data that they have procured by regulating the access to EPHI and off-balance backups
- Technical: The automation process, such as authentication controls and securing data with encryption during its transfers.
Though Encryption’s implementation is not compulsory under the guidelines issued by HIPAA, it still recommends to the medical and healthcare industry to implement it whenever they feel suitable.
Data solitude is the construction brick in every organization.
Failure to secure users’ personal data can generate harmful consequences to the company’s reputation and goodwill.
Introducing the Machine Learning model in encryption takes a little longer to expand its branches for other use cases.
Machine learning has widened up the horizons for controlling privacy, along with providing innovative solutions.