Co-authors: Krishnaram Kenthapadi, Thanh Tran, Mark Dietz, and Ian Koeppe Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications. With the growing appreciation of the impact of data breaches and comprehensive data regulations,...
trust engineering Articles
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- Topics:
- trust engineering,
- Analytics,
- research,
- Data
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LinkedIn members entrust us with their personal data and we are committed to working hard every day to maintain that trust within a safe, professional environment. One crucial aspect to earn and maintain that trust lies in how well we secure our online systems and protect our data from unauthorized exposure. LinkedIn runs a microservice architecture, in which...
- Topics:
- scale,
- Security,
- trust engineering,
- Data
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LinkedIn’s Help Center boasts a personalized experience aimed at getting our members the help they need, finding the answers to their questions, and empowering them to be more productive and successful. Despite having a wealth of customized content, members are still faced with a problem when they need help: they must stop what they’re doing and go to the Help...
- Topics:
- trust engineering,
- Search,
- Product Design,
- Ember
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LinkedIn is committed to building a safe, trusted, and professional environment. Building the infrastructure to detect and mitigate...
- Topics:
- Security,
- trust engineering
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To maintain a safe and trusted professional community on LinkedIn, we require that every LinkedIn profile must uniquely represent a...
- Topics:
- machine learning,
- Security,
- trust engineering