Data sanitization
[2] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to another individual.
While the practice of data sanitization is common knowledge in most technical fields, it is not consistently understood across all levels of business and government.
With the increasingly connected world, it has become even more critical that governments, companies, and individuals follow specific data sanitization protocols to ensure that the confidentiality of information is sustained throughout its lifecycle.
These were selected as they typically also fall under government regulations, and therefore NIST (National Institute of Standards and Technology) guidelines and policies would also apply in the United States.
[9] Based on these articles and NIST 800-88 recommendations, depending on its data security level or categorization, data should be:[3] The International Information Systems Security Certification Consortium 2020 Cyber Workforce study shows that the global cybersecurity industry still has over 3.12 million unfilled positions due to a skills shortage.
This policy will require a high-level management champion (typically the Chief Information Security Officer or another C-suite equivalent) for the process and to define responsibilities and penalties for parties at all levels.
For small business and those without a broad cyber background resources are available in the form of editable Data Sanitization policy templates.
This task should be easy to accomplish as most government contractors are already required to perform annual Information Security training for all employees.
This method uses mechanical shredders or degaussers to shred devices, such as phones, computers, hard drives, and printers, into small pieces.
Degaussing is most commonly used on hard disk drives (HDDs), and involves the utilization of high energy magnetic fields to permanently disrupt the functionality and memory storage of the device.
When particularly sensitive data is involved it is typical to utilize processes such as paper pulp, special burn, and solid state conversion.
This provides a greater ease of use, and a speedier data wipe, than other software methods because it involves one deletion of secure information rather than each individual file.
For instance, a hard drive utilizing cryptographic erasure with a 128-bit AES key may be secure now but, in 5 years, it may be common to break this level of encryption.
The process of data erasure involves masking all information at the byte level through the insertion of random 0s and 1s in on all sectors of the electronic equipment that is no longer in use.
[15] Data erasure often ensures complete sanitization while also maintaining the physical integrity of the electronic equipment so that the technology can be resold or reused.
A number of storage media sets support a command that, when passed to the device, causes it to perform a built-in sanitization procedure.
[19][20] There have been a few reported instances of failures to erase some or all data due to buggy firmware, sometimes readily apparent in a sector editor.
If data is not properly removed from cloud storage models, it opens up the possibility for security breaches at multiple levels.
Inadequate data sanitization methods can result in two main problems: a breach of private information and compromises to the integrity of the original dataset.
There have been iterations of common data sanitization techniques that attempt to correct the issue of the loss of original dataset integrity.
Robust research was conducted on the efficacy and usefulness of this new technique to reveal the ways that it can benefit in maintaining the integrity of the dataset.
[30] One approach to achieve this optimization of privacy and utility is through encrypting and decrypting sensitive information using a process called key generation.
For companies that are seeking to share information with several different groups, this methodology may be preferred over original methods that take much longer to process.
[31] Certain models of data sanitization delete or add information to the original database in an effort to preserve the privacy of each subject.
[25] An important goal of PPDM is to strike a balance between maintaining the privacy of users that have submitted the data while also enabling developers to make full use of the dataset.
One type of data sanitization is rule based PPDM, which uses defined computer algorithms to clean datasets.
Deep learning is able to simplify the data sanitization methods and run these protective measures in a more efficient and less time-consuming way.
There have also been hybrid models that utilize both rule based and machine deep learning methods to achieve a balance between the two techniques.
The healthcare industry is an important sector that relies heavily on data mining and use of datasets to store confidential information about patients.
The use of electronic storage has also been increasing in recent years, which requires more comprehensive research and understanding of the risks that it may pose.
