4 Data Privacy Protection Techniques
GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) are two of the most well known standards for data protection.
GDPR is legislated by the EU governing all data related to EU persons & businesses, while HIPAA sets the standards for protecting sensitive health data in the USA. Both these standards address and protect Personally Identifiable Information (PII), designed to prevent abuse of data and gain trust in the systems. Beyond mere compliance, innovators should design systems to gain the trust of people.
What are some of the techniques that have been developed to protect personal data privacy?
4 Data Privacy Protection Techniques :
- Anonymization: Anonymization has been leveraged to mask information that can help identify individuals within a sample. While first-gen anonymization techniques like k-anonymity and l-diversity were shown susceptible to identification through attribute data, novel techniques like t-closeness are focused on limiting the degree to which information can be inferred about an individual from a dataset.
- De-identification: De-identification techniques are focused on eliminating information that can be used to, directly or indirectly, through the use of complementary datasets, identify or re-identify an individual. De-identification calls for an accountable governance structure, and risk evaluation processes that take the data sharing strategy and class size of the input data into account. These approaches can be used to facilitate data sharing when the consent to share personal data is not given to a third party, but the ability to share this information can result in gains for either or both parties.
- Privacy-enhancing: Privacy-enhancing techniques vary by the nature of the use case and the architectural configuration in question. Some examples include homomorphic encryption, which is leveraged to maintain anonymity by encrypting the input data and inferring through decryption of the output, two-factor authentication and identity access management techniques that limit access to the data, zero-knowledge proofs, differential privacy mechanisms that ensure non-identifiability by adding noise, multiparty-computation, where the input data is not visible to individual parties in its entirety, and federated analysis, in which multiple parties gather to collate insights leveraged from the input data rather than the input data itself.
- Process near the data: A fairly recent approach to ensuring privacy is to send the code to the data itself, instead of the other way around. A notable example is the OPen ALgorithms (OPAL) project, where the privacy of the individuals is ensured at the processing stage by letting third parties submit algorithms that are then trained on the data. While such techniques remain susceptible to model inference attacks, they also indicate the role that a data-blind intermediary can play in helping unlock the power of big data without compromising on the privacy of individuals and simplifying the process of obtaining consent and maintaining agreements.
Most big-data systems will require a mix of these approaches to build privacy-ensuring mechanisms into the architecture by design. We are at a turning point where innovators have a unique opportunity to build a new era of privacy compliant and trustworthy AI technologies by incorporating data governance from the very beginning of project conceptualization.
About Zoi Meet
Zoi Meet develops data privacy compliant speech recognition and analysis technology. Especially for enterprises and services that seek to unleash the untapped potential of knowledge & information within our spoken communication – such as meetings, sales calls, video conferencing, conferences, telehealth etc – Zoi Meet advocates AI privacy, scalability & accuracy with our patent-pending innovation built on data privacy compliance and trust.For more information, kindly contact Zoi Meet at firstname.lastname@example.org or reach out to :