How to ensure that data security practices are legally adequate. Privacy advocates argue that it is the scale of data collection that can potentially threaten individual privacy in new ways. Sign up for the free insideBIGDATA newsletter. Data is needed to train machine learning algorithms, and in many cases is the key differentiator from competitors. It is a recipe for an expensive lawsuit or government investigation that could be fatal to a young startup business. For example, The New York Times wrote an investigative piece on location data. If your data scientists find a new use for the data you’ve collected, you must return to the data subjects and get them to agree to an updated privacy policy. This sort of noncompliance was the basis for the $5 billion fine assessed against Facebook last year. The Big Data Conundrum One of the most contentious privacy concepts for enterprises is the idea of obtaining consent or permission to collect and use personal data. Interview: Dr. Bhushan Desam, Director, Global AI Business at Lenovo, AI World – Industry’s Premier Event Focused on Enterprise AI – Boston, December 11-13. That’s a large number, but compare it with 145 million people whose birth dates, home and email addresses, and other information were stolen in a data breach at eBaythat same year. Privacy compliance attorneys need to be directly involved in the product design effort. Although the data was anonymized, the Times was able to identify the data record describing the movements of New York City Mayor Bill de Blasio, by simply cross-referencing the data with his known whereabouts at Gracie Mansion. Yet, personal data, that is, data relating to an individual, is also subject an increasing array of regulations. … Lawmakers Respond to Big Data Privacy Concerns. For example, if a data record has the name “John Smith” associated with it, a hash operation may to convert the name “John Smith” into a numerical form which is mathematically difficult or impossible to derive the individual’s name. The enterprise search industry is consolidating and moving to technologies built around Lucene and Solr. Selected papers will be published in a special issue of the Stanford Law Review Online and presented at an FPF/CIS workshop, which will take place in Washington, DC, on September 10, 2013. As our ability to collect and store vast quantities of information has increased, so too has our capacity to process this data to discover breakthroughs ranging from better health care, a cleaner environment, safer cities, and more effective marketing. Apple introduced privacy labels to apps in the Mac and iOS App Stores. Sometimes consumers adjust to the new stream of data (Facebook’s Newsfeed), and other times they simply do not (Google Buzz). What processes and safeguards need to be in place to properly handle personal data. The ability to remove personal information has to be baked into the system design at the outset. In this special guest feature, Joseph E. Mutschelknaus, a director in Sterne Kessler’s Electronics Practice Group, addresses some of the top data privacy compliance issues that startups dealing with AI and ML applications face. The California Consumer Privacy Act (CCPA), which is widely viewed the toughest privacy law in the U.S., came online this year. The result is a regime where entities collect data first and ask questions later. Thus, when Big Data opportunities and privacy concerns collide, important decisions are made ad hoc. That said, often the usefulness of data is premised on being able to identify the individual that it is associated with, or at least being able to correlate different data sets that are about the same individual. Subscribe to receive our monthly newsletter and information about upcoming events, Big Presidential Campaigns Raise Big Privacy Issues. As Big Data technologies are emerging at very fast pace, it is also creating space for security and privacy issues. He also assists with district court litigation and licensing issues. This example illustrates the inherent limits to anonymization in dealing with privacy compliance. Takeaway: To succeed in the new data economy, companies are collecting massive amounts of consumer data. This can be tricky, particularly in cases where the underlying data is surreptitiously gathered. The big challenge has become that the data custodians who spend time making sure data is handled properly — because a lot of data is not handled by a human, it’s handled by automated processes — [have] flaws … Noting that credit card limits and auto insurance rates can easily be crafted on the basis of aggregated data, tech analyst and author Alistair Croll cautions that individual personalization is just “another word for discrimination.” Advocates worry that over time, Big Data will have potentially chilling effects on individual behavior. The regulation’s most recent draft proposal, drafted by Jan Philipp Albrecht, Rapporteur for the LIBE Committee,  restricts individual profiling, which is defined as “any form of automated processing of personal data intended to evaluate certain personal aspects relating to a natural person or to analyse or predict in particular that natural person’s performance at work, economic situation, location, health, personal preferences, reliability or behaviour.” This sort of limit on “automated processing” effectively makes verboten much of the data that scientists and technologists see as the future of Big Data. There is an inherent conflict here. These "nutrition labels" aren't a panacea for Big Tech's data privacy woes, but rather a measure of triage. 2. Computer scientists may recognize a technique called a one-way hash as a way to anonymize data used to train machine learning algorithms. What is needed in a compliant privacy policy. Hackers and thieves. 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