Source: https://valuementor.com/blogs/understanding-the-growing-concern-around-data-privacy
With increased importance in numerous industries, data analytics have enabled various businesses to achieve greater abilities in terms of collecting extensive information and studying it. However, the same analytical power has compromised the need for ensuring privacy of the provided data by individuals and users. As an appropriate approach to address such concerns, differential privacy has been utilized to quantify privacy risk in analytics, providing a formal way to limit exposure of personal information in query responses. At the same time, applying such methods to sensitive data in the contexts of continuous collection of users’ telemetry and healthcare analytics can create substantial difficulties in terms of fulfilling the requirements of epsilon-privacy and data utility. On the whole, differential privacy mechanisms can be applied to numerous types of databases. Generally, such methods are based on adding carefully calibrated noise to the results of queries or analytical procedures to ensure that individual contributions to the findings remain statistically undetectable. Typically, algorithms involve adding random variables, which usually follow a Laplace or Normal distribution, to the result of a query or computational output. Overall, a specific value of epsilon, which determines a privacy loss budget, is introduced to define the amount of noise and thus the level of privacy. With smaller values of epsilon meaning higher privacy levels and lower data utility, finding the appropriate balance is essential. Nevertheless, as an outcome of the described nature of differential privacy, there is a conflict between privacy level provided by the methods and analytical utility of results. Specifically, in large-scale analytics, the term data utility usually refers to the degree of accuracy of obtained results or the quality of obtained insights into the studied data. Therefore, finding appropriate levels of noise addition that would fulfill requirements of privacy and utility remains challenging. In cases where large-scale analytics involves high dimensions, i.e., numerous variables, and high volume of data, finding an acceptable level of privacy becomes more difficult. Such data settings are common for many applications in the big data domain. For example, in analyses based on users’ telemetry, the data collected in such studies are of major importance for most companies. In such contexts, differential privacy provides a useful opportunity to transform raw information into generalized findings, which would still hold true for the majority of users. This way, businesses can still operate with useful results while fulfilling the privacy concerns of many users. In turn, healthcare analytics represent a more difficult application of differential privacy mechanisms. Indeed, the majority of studies in healthcare settings aim to help medical staff and researchers find new treatments for various conditions. In such contexts, data utility is of even higher importance, given that finding useful patterns in data is the primary goal of analysis. Additionally, healthcare data sets tend to have a smaller size as compared to telemetry data sets collected from millions of users. Therefore, a small addition of noise, which would significantly compromise analytical utility, should not be applied. In general, researchers and clinicians trying to utilize differential privacy in medical studies should work on finding an appropriate balance in terms of privacy and utility. At the same time, modern algorithms provide such entities with opportunities to address this challenge. For example, multiple privacy-preserving data analytics solutions can help generate useful statistical findings, thus helping researchers obtain viable insights. In conclusion, while differential privacy presents a major opportunity to fulfill the growing needs for ensuring privacy of participants in large-scale data analytics, there are certain difficulties associated with the implementation of such approach. Indeed, the main challenge in utilizing differential privacy mechanisms is addressing the trade-off between balancing sufficient levels of data utility and ensuring data privacy. However, given the increasing need for improving data analytics in various domains, methods for finding such a balance are paramount. Sources: https://www.nist.gov/publications/guidelines-evaluating-differential-privacy-guarantees https://pmc.ncbi.nlm.nih.gov/articles/PMC8449619/