A new breed of technology that allows the processing and analysis of data without revealing sensitive data and algorithms
The security and risk management leaders are beginning to focus on their privacy programs for scalability and better performance despite limited financial budgets. It is comparatively easier to cut down on other business areas, but the risk of tarnishing brand reputation due to privacy incidents is non-negotiable today.
Gartner expects companies that integrate privacy user experience into their CX to reap the benefits of trust by 2023. These companies are expected to witness over 20 percent digital revenue. The simple fact remains that customers that trust more, spend more. Experts state that digital operations involving personal identifiable information need to address customer expectations about data safety and increase the depth of privacy regulations. Looking at both internal and external data sources to mine for insights require personal data for data monetization, business intelligence, and fraud analytics.
The report also predicted that 50 percent of organizations will begin to adopt it in multi-party data analytics use cases. This is where privacy-enhancing computation capabilities and differential privacy come into the enterprise ecosystem.
They are a new breed of technology that allows the processing and analysis of data without revealing sensitive data and algorithms. Innovative techniques including differential privacy, homomorphic encryption, private set intersection, and zero-knowledge proof are the possible methods to work around data privacy and its unstable legal framework.
However, the stored data can become vulnerable to data breaches as the data will mostly be shared in untrusted environments.
Although many of these privacy-enhancing computation techniques are being tested by enterprises, several companies are sceptical about the legal liabilities that are seen as a package deal with its massive benefits.
Differential privacy is one of the most popular techniques today as it does not allow hackers to access data to reverse engineer data elements and compromise company data. The more sensitive information organizations possess, deal with real-time high-performance data processing, and have high levels of AI model accuracy reliance; the more crucial it is to invest in differential privacy.
The nascent level of the technology requires constant measurement. It also needs extensive data and sustained investment to showcase results. Additionally, scaling the deployment require strategic planning and awareness across organizations. Companies that recognize the value of differential privacy also make them a target by cybercriminals. The monetary liabilities from such crucial data leaks can be powerful enough for hackers to incessantly try and break the system. Hence, the cybersecurity teams of those companies must always stay on high alert for possible hacking attempts. Experts strongly recommend continuous monitoring and fine-tuning the changing nature of data. Yet, Gartner reported privacy-enhancing computation to be a 2021 strategic technology trend.
On the other hand, organizations that want to build trust in a brand-customer relationship internally are hesitant to share information with an outside source. It causes a further issue of lost opportunity. Experts opine that without the flexibility of relying on outside innovation that can pay rich dividends, scalability and growth are limited.
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