Privacy-enhancing computation.
An enormous amount of data is leaked into the digital sphere, every single minute. From wearables that collect information about our bodies to landmarks noted by digital grids, individuals and businesses are connected extensively. To boost the business productivity and workflow digitalization of businesses, privacy-enhancing computation is a tool that aims to keep all data confidential when they reside in third-party hardware. This reflects the rise of digital trust that unlocks privacy to create business value. Privacy-enhancing computation leverage data asset monetization while ensuring privacy concerns of customers. When analysing the adverse results of privacy concern negligence, businesses must pay fines for non-righteous acts under data protection laws. The fame and fortune of those businesses may get affected by the flaw of secure data handling. At the same time, misrepresentation of personal data and human dignity violations can immensely be a threat to users who trust their service. These are the reasons that reveal the importance of implementing privacy-enhancing computation which safeguards the economic potential of businesses and privacy concerns of the public as well. Certain modern data protection technologies are used in so-called 'privacy-enhancing computation’.
Homographic encryption is a computation done on encrypted data without ever decrypting. That means you can outsource computation without giving access to data. Here data is given to the service provider, data is exposed, still making it vulnerable to cyber criminals’ privacy violations. In the line of days, one customer makes an order for a large pizza from pizza hut. If he does this often, the app will collect data of their location, preference, time of preference, lifestyle, financial capability, and more. Maybe they do not care about an app knowing their data, since convenience outweighs the risks. But regarding health care scenarios and financial backups, sensitive information should not be shared. Thus, AI machine learning, and computation functions analyse the data but enable the essence of privacy through homographic encryption where data is analysed but not shared. Service is granted without knowing which information you are returning.
In the MPC(Multi party computation) technique, organizations input data which is split into pieces and masked by adding random numbers. The pieces are sent to multiple independent computing devices or servers. Throughout data processing, the servers never receive the organization's data. All those data are new tokens and meaningless on direct view. Only the encoded, aggregated amounts are then compared. MPC makes use of cryptographic protocols that guarantees data privacy and trust, unlike traditional cloud computing. It allows organizations to work together without disclosure of underlying data according to MPC protocol. It can help to examine big questions and solve complex problems. In traffic plans that help assuage drive time headaches, predicting health outcomes to treat patients, forecasting customer choices in a market and in all other sectors, applications learn from MPC outcomes which is very useful for specific function problem-solving. All these happen while maintaining privacy in a data-driven world.Obfuscation is another technique where the server, obscure the intended meaning and communicate the message connoted ambiguously. Doctors who are writing jargons to conceal unpleasant facts in prescription are similar to the functionality of obfuscation. In the data minimization technique, only minimum adequate data is collected and personal data is removed. Certain data is restricted to access if they are not required for legitimate business purposes. Personally identified elements are removed in the data processing. In AI-based synthetic data generation, artificially trained data is produced from the original data set with all similar features, same correlation, same structure, same dependencies, and behaviours. Here there will be no direct one-to-one relationship with the original data set, where only the new complete similar set is generated. This synthetic data is going to be an anonymous outcome that is free to use, share and monetize.
With the digital enhancement, users are not going to be always preyed. In this way, the privacy-enhancing computation preserves the crown jewel data that people do not want to share.
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