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Stephanie Diehl is an associate in the Litigation Department in New York. She is a trial lawyer and registered patent attorney whose practice focuses on patent litigation and IP counseling. Stephanie’s practice spans a wide range of technological fields, including software, payment processing, computing systems, cellular phone technology, and medical devices. Stephanie has represented clients in district courts and in the U.S. International Trade Commission. Additionally, Stephanie has represented clients before the Patent Trial and Appeal Board and has experience in preparing and prosecuting patent applications.

As we mentioned in the early days of the pandemic, COVID-19 has been accompanied by a rise in cyberattacks worldwide. At the same time, the global response to the pandemic has accelerated interest in the collection, analysis, and sharing of data – specifically, patient data – to address urgent issues, such as population management in hospitals, diagnoses and detection of medical conditions, and vaccine development, all through the use of artificial intelligence (AI) and machine learning. Typically, AIML churns through huge amounts of real world data to deliver useful results. This collection and use of that data, however, gives rise to legal and practical challenges. Numerous and increasingly strict regulations protect the personal information needed to feed AI solutions. The response has been to anonymize patient health data in time consuming and expensive processes (HIPAA alone requires the removal of 18 types of identifying information). But anonymization is not foolproof and, after stripping data of personally identifiable information, the remaining data may be of limited utility. This is where synthetic data comes in.

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