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8 Use Cases
The BWWC began using a multi-party computation (MPC) system developed in partnership with the Hariri Institute in 2017 to enable organisations to anonymously report gender pay gap information. The data collected included gender, ethnicity, length of service, annual compensation, and performance pay. The latest report, produced in 2019, includes data for 136,437 employees from 123 organisations, representing 13% of the Greater Boston workforce. The confidentiality provided by the MPC solution has encouraged a greater number of companies to participate in the study, increasing from 69 companies in 2016 to 123 in 2019. User experience was also important in encouraging participation with the user interface providing a familiar spreadsheet that can be filled with data manually or via copy-paste. The statistics derived through this process have shown that the gender pay gap in the Boston area is even larger than previously estimated by the U.S. Bureau of Labor Statistics.
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Sugar beet farmers in Denmark have contracts determining how much beet they produce. All beet produced goes to Danisco, the only sugar producer in Denmark. The EU significantly reduced beet subsidies, meaning the country needed to develop a competitive market for trading production rights. A system was developed leveraging multi-party computation to enable confidential bidding to compute a trading price based on supply and demand. This enabled the production quota to be redistributed accordingly, whilst details of individual beet farmers bids remained confidential. 80% of farmers surveyed said that this confidentiality was important to them. The first auction took place in 2008 and is considered the first large-scale, practical application of multi-party computation.
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In 2011, the ITL proposed collecting key financial metrics from its member companies in order to better understand the state of the telecoms sector. Members expressed concern over the confidentiality of the metrics as they would be sharing them with competitors. ITL chose to partner with cybersecurity firm Cybernetica, who were able to deploy their Sharemind secure computing platform to enable the analysis to be done whilst protecting confidentiality. 17 companies participated, uploading their metrics to the Sharemind platform, which distributed the data across three “computing parties” (CPs). These CPs performed the desired analysis using a multi-party computation protocol to ensure confidentiality. The final results of the analysis were shared with the ITL who disseminated accordingly. The distributed nature of the computation meant no party, including the ITL, ever had direct access to another party’s metrics.
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The public sector gathers workforce information from the private sector (e.g. worker name, date of birth, employment start date). Insurance companies have claims information from their private sector clients (client industry, client size, client workforce size). While the public sector may retain a centralised database, each insurance company only has access to its own data. The public sector and the private sector do not share claims related data with each other. The insurance industry knows that there is a correlation between a corporate organisations average workforce age compared to the average cost of claims by industry. Using the AIR Platform, users are able to privately and securely access both the public sector data and private insurance data. The data remained in situ and under the control of each data holder. Algorithms revealed correlations that improved the participating insurance company’s risk model by 1 to 4%, achieving profitability improvement of US$ 700,000 to US$ 2,800,000 million. The resulting knowledge of correlation was used by the insurance company to provide proactive risk management advice to its customers (such as recommending measures to be taken on construction sites that typically lead to a percentage reduction of workplace accidents).
The Bureau has leveraged differential privacy to minimise the risk of identification of individuals when publishing statistics from the 2020 Census. The total population in each state will be as counted, but all other levels of geography - including congressional districts down to townships and census blocks - could have some variance from the raw data as a result of noise-injection to facilitate differential privacy. Setting the value of the privacy budget has not been trivial. The value chosen by the Census Bureau’s Data Stewardship Executive Policy committee was far higher than those envisioned by the creators of differential privacy. There are further challenges, with the National Congress of Native Americans expressing concern that DP could adversely affect the quality of statistics about tribal nations.
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Enveil, a PETs company, has partnered with DeliverFund, a counter-human trafficking intelligence organisation, to use homomorphic encryption based technology to provide access to a large human trafficking database in the US. DeliverFund’s product reduces the time it takes to identify victims and who exploits them. The use of Enveil’s technology will allow them and their partner organisations to better access data on counter-human trafficking without exposing PII or other sensitive data. Users of the platform will be able to cross-match and search on DeliverFund’s database without revealing the contents of their search or compromising the security of the data they are searching on.
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The public sector wanted to make a new analytics algorithm available to the public which would be made available as open source to anyone wishing to develop a contract review or access to justice solutions. Accessing large volume of relevant data to train this new algorithm was the challenge. A group of participants from the public sector and private sectors made their data accessible using the RegulAItion Platform. They were able to develop and deploy a new algorithm on each of their data without ever sharing, moving or pooling their data. The collaborative project was completed in 12 weeks. The new algorithm contained 6 models. These models were decomposed into private local models and global shared models.
The Indonesian Ministry of Tourism has made use of statistics on mobile phone positioning data to better understand cross-border tourism activity in the country. The task required that information by multiple mobile network operators was analysed together to understand when users cross between different regions, which are covered by different operators. Mobile positioning data is generally protected, due to the sensitivity of information about individuals’ mobility. The data was encrypted and aggregated using Sharemind, a privacy technology from Cybernetica. The mobile positioning data came from a system delivered by Estonian data analytics company, Positium, for the Ministry of Tourism. The Sharemind platform uses a trusted execution environment and does not allow access to unencrypted data nor encryption keys at any stage. The aggregated statistics about the number of users, and the overlap in use between the two largest mobile networks is now used by the Ministry of Tourism as a basis for the creation of tourism statistics.