Unlocking the Potential of Zero-Knowledge Proofs in Blockchain
Zero-Knowledge Proofs (ZKPs) are becoming a standard within blockchain technology, helping to maintain transactional privacy and integrity, highlighting their potential to create a more secure and...
View ArticleValidating Factual Personal Information
We are called upon, frequently, to validate personal information: name, address, phone number, birth date, national identification number, birthplace, employment, educational record, and more. Often...
View ArticleThe Campaign Against Deepfakes
Politics has never had a great reputation. Candidates fib, lie, and toss insults at each other with near-impunity. Opponents bend, distort, and misrepresent each other’s words. Yet in the age of...
View ArticleDefending the Business
Nothing brings an issue into focus like the repeated assertion of the significance of its risk. The enterprise has no greater risk than a cyber incident, such as a ransomware attack, data breach, or...
View ArticleVerifying Correctness
Cryptographer and 2022 ACM Prize winner Yael Tauman Kalai is keenly aware of the trade-offs that often must be made between security and computational efficiency. Kalai, who works as a Senior...
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Internet of Things Security and Privacy Labels Should Empower Consumers
The White House launched a new U.S. Cyber Trust Mark in July 2023, unveiling the design and announcing the U.S. Federal Communications Commission (FCC) would be soliciting comments on a wide range of...
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Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective
According to a report from Lloyd’s Register Foundation,a at present, cybercrime is one of the biggest concerns of Internet users worldwide, with disinformationb ranking highest among such risks (57%...
View ArticleTechnical Perspective: Hiding Secrets in Programs
Can we create computer programs that do not reveal anything about their inner workings? This is the goal of program obfuscation. An obfuscator is a compiler that transforms a program into an...
View ArticleIndistinguishability Obfuscation from Well-Founded Assumptions
1. Introduction Consider the polynomial f1(x,y)ɛℤ[x,y] that is computed as follows: f 1 ( x , y ) = ( x + y ) 16 − ( x − y ) 16 Alternatively, contemplate the polynomial f2(x,y)ɛℤ[x,y] that is...
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The Science of Detecting LLM-Generated Text
Recent advancements in natural language generation (NLG) technology have significantly improved the diversity, control, and quality of large language models (LLM)-generated text. A notable example is...
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