Topics in Digital Privacy

Applications of Multiparty Computation

Digital privacy continues to evolve as cryptosystems transition from academic theory to practical and applicable uses. One of the most common and user-friendly ways to safely share data is through secure multiparty computation (MPC). MPC enables multiple parties to jointly compute their inputs, or data, without sharing their underlying values.

Large-scale industries such as health care, finance, government, business, education, and public policy have discovered numerous real-world applications of multiparty computation. Secure MPC is an ideal protocol for addressing large-scale and complex computational needs. At the same time, MPC helps companies, institutions, and organizations protect the security of their data without relying on a third party.

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Advantages of Homomorphic Encryption

Data security is vital in today’s digital world, and data encryption is a crucial component. Homomorphic encryption is a security method that lets you directly perform computations on encrypted data without decryption, allowing sensitive information to remain private.

Homomorphic encryption can help organizations maintain a high level of data security without reducing productivity or violating protocols. It can greatly increase data privacy and security in a variety of applications.

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Data Privacy and Ethics: Building Trust in the Information Age

The ongoing shift from manufacturing toward information-based economies has increased the drive for collection, storage, and use of vast quantities of personal data. Entities managing this data must comply with privacy regulations specific to industry and location. However, growing public awareness and concern about misuse of personal data is leading organizations to revisit governance on data privacy and ethics. Data security has expanded beyond the legal realm and is now recognized as a morality issue and key component of stakeholder confidence.

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Differential Privacy and Applications

A data breach can lead to fraud, identity theft, and millions of dollars in damages, not to mention a soiled professional reputation. Breaches are on the rise—in fact, the Verizon 2021 Data Breach Investigations Report found 5,258 confirmed data breaches across 20 industries.

With sensitive data on the line, it’s no surprise that the federal government has enforced privacy laws since the 1970s. A lot has changed since the first privacy law—in a modern society that runs on data, how can companies and individuals ensure their privacy?

As organizations collect more data, they must provide more data privacy as well. One security method gaining popularity for its unique handling of security is differential privacy, and applications of this method are widespread. It allows complex data analysis without risking privacy loss.

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Digital Ethics and Privacy Technology: How to Ethically Manage Data

The collection and analysis of personal data undeniably benefits both consumers and the greater social good. From governments’ detection of potential terrorist activities to supermarkets’ ability to keep popular items in stock, big data applications use forecasting and predictions to effectively solve problems.

As with most technologies, though, solving one problem creates a host of others. In the case of big data collection and analysis, one of the most serious problems is potential violations of data ethics. Data ethics refers to the use of data in accordance with the wishes of the people whose data is being collected.

Organizations are facing growing pressure to handle consumer data responsibly and transparently. As such, they need to attend to questions of data usage, digital ethics, and privacy technology. Indeed, organizations should not only understand the ethical issues behind data collection and the current regulatory environment—they should proactively implement a plan and practice of data ethics.

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Ethical Issues Related to Data Privacy and Security: Why We Must Balance Ethical and Legal Requirements in the Connected World

Different industries, organizations, and governing bodies view the issue of data privacy differently. Additionally, ethical issues related to data privacy and security can change how a group of people thinks about data dissemination. In emergency situations, some individuals could value a fast and informed response more than they value data privacy.

Because the opinions and ethics surrounding data privacy are not constant, it can be challenging for governing authorities to enforce legal requirements. Governance around data privacy and security is an important part of society, however, to protect individuals. To maintain ethical guidelines and protect the general public, governing bodies should weigh the costs and benefits around data privacy and security, being willing to adjust when needed.

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Homomorphic Encryption Use Cases

The way we communicate is changing with the fifth generation (5G) of telecommunications. With growing numbers of mobile users, cloud computing, and more, data privacy is more important than ever.

Enter homomorphic encryption, a cryptographic scheme that allows data processing without decryption. A homomorphic encryption scheme can be applied to various sets of data so only pieces are shared with those authorized to see them, increasing data privacy and security. From business to health care applications and beyond, the development of this technology suggests many future homomorphic encryption use cases.

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Principles of Open Data Governance

It’s almost impossible to imagine modern life without data sharing. Big data is big business; data is at your fingertips every second of the day, with just a few short clicks on your smartphone or laptop. The World Economic Forum estimates that by 2025, 463 exabytes of data will be created daily.

Businesses and organizations need data strategies to properly manage their valuable data. Executive leadership teams use data to make important business decisions. Governments harvest data constantly and must manage who has access to it. But how is all this data managed, and what data standards are there?

The push for open data introduces more questions about privacy and security into the mix. As more data migrates to the cloud, businesses and governments must manage and regulate data, its security, and its accessibility. Open data governance attempts to regulate these issues as society strives to build a more equitable world through access to information.

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Types of Homomorphic Encryption

At its root, homomorphic encryption is a form of encryption that permits users to perform computations on encrypted data without decrypting it first. Essentially, homomorphic encryption turns a set of data into code to allow data analysis without sacrificing privacy. There are three types of homomorphic encryption, and they use variations or extensions of public key cryptography to encrypt and decrypt data.

In practice, homomorphic encryption allows you to subdivide encrypted data so you have one key to decrypt the entire data set and several other keys that only decrypt the subparts. This gives you the opportunity to have different pieces of encrypted data to be worked on or viewed by different people independently. The end result is more direct control of the privacy of the encrypted data.

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What Is Differential Privacy?

When the United States gathers census data or a hospital shares medical information of patients for data analysis, personal information of participants is at risk. From each individual database, the risk can be relatively small. Multiple databases together containing small pieces of anonymized information can potentially be used to identify an individual participant. This is the problem differential privacy has been designed to solve.

At its roots, differential privacy is a mathematical way to protect individuals when their data is used in data sets. It ensures that an individual will experience no difference whether they participate in information collection or not. This means that no harm will come to the participant as a result of providing data.

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What Is Homomorphic Encryption?

Data sharing and storage capabilities are more secure than ever before with advancements in encryption technologies. However, traditional encryption schemes are limited in their cloud computing abilities and therefore present security concerns. For this reason and many others, telecommunications and cybersecurity experts are monitoring the technological developments of homomorphic encryption.

What is homomorphic encryption? Homomorphic encryption systems allow data to be analyzed and processed on a ciphertext rather than the underlying data itself. In other words, encrypted data can be accessed but never decrypted.

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What Is Multiparty Computation?

With so much of the world’s business being conducted electronically, the amount of data has never been greater. Still, this abundance of data—which is moving faster than ever thanks to advancements in research areas connected to 5G technology—can be used to calculate even more numerical insight. The issue then is how to share and utilize data without sacrificing privacy.

This is where the answer to “what is multiparty computation?” arrives.

At its heart, multiparty computation (MPC) allows for multiple parties to share data for computing tasks without revealing each other’s data. All parties are privy to the output of the computing tasks, but no party learns anything about others.

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What Is Zero Trust Architecture?

At a time when remote work is becoming more prevalent, a growing number of bad actors are stealing data, implanting malware, and launching ransomware attacks. Establishing zero trust architecture in cyber networks is crucial to preserving data integrity and protecting data access.

What is zero trust architecture, and how is it better? Traditional network cybersecurity architecture can allow unwelcome incursions by assuming the trust of inquirers before verifying the right to access applications and data. Conversely, zero trust security solutions deny user access to any inquirer—whether people or software systems—up front. Trust must be gained and is achieved internally via systems that rely on trust algorithms.

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