MIT Open Algorithms
The Open Algorithms (OPAL) paradigm seeks to address the increasing need for individuals and organizations to share data in a privacy-preserving manner. Data is crucial to the proper functioning of communities, businesses and government. Previous research has indicated that data increases in value when it is combined. Better insight is obtained when different types of data from different areas or domains are combined. These insights allows communities to begin addressing the difficult social challenges of today, including better urban design, containing the spread of diseases, detecting factors that impact the economy, and other challenges of the data-driven society.
Today there are a number of open challenges with regards to the information sharing ecosystem:
- Data is siloed: Today data is siloed within organizational boundaries, and the sharing of raw data with parties outside the organization remains unattainable, either due to regulatory constraints or due to business risk exposures.
- Privacy is inadequately addressed: The 2011 World Economic Forum (WEF) report on personal data as a new asset class finds that the current ecosystems that access and use personal data is fragmented and inefficient. For many participants, the risks and liabilities exceed the economic returns and personal privacy concerns are inadequately addressed. Current technologies and laws fall short of providing the legal and technical infrastructure needed to support a well-functioning digital economy. The rapid rate of technological change and commercialization in using personal data is undermining end-user confidence and trust.
- Regulatory and compliance: The introduction of the EU General Data Protection Regulations (GDPR) will impact global organizations that rely on the Internet for trans-border flow of raw data. This includes cloud-based processing sites that are spread across the globe.
The following site has a demo of MIT OPAL
- T. Hardjono and A. Pentland, MIT Open Algorithms, in Trusted Data: A New Framework for Identity and Data Sharing, MIT Press 2019 (PDF).
- A. Pentland, Social Physics: How Social Networks Can Make Us Smarter. Penguin Books, 2015.
- A. Pentland, T. Reid, and T. Heibeck, “Big Data and Health - Revolutionizing Medicine and Public Health: Report ofthe Big Data and Health Working Group 2013,” World Innovation Summit for Health, Qatar Foundation., Tech. Rep., December 2013 (PDF)
- Y. A. de Montjoye, E. Shmueli, S. Wang, and A. Pentland, “openPDS: Protecting the Privacy of Metadata through SafeAnswers,” PLoS ONE 9(7), pp. 13–18, July 2014, https://doi.org/10.1371/journal.pone.0098790.
- Y. A. de Montjoye, J. Quoidbach, F. Robic, and A. Pentland, “Predicting personality using novel mobile phone-based metrics,” in Social Computing, Behavioral-Cultural Modeling and Prediction (LNCS Vol. 7812). Springer, 2013, pp. 48–55.