GMV launches "uTile" to speed up the use of trustworthy artificial intelligence

Data ethics and privacy are sine qua nons of a trustworthy use of artificial intelligence (AI). To quicken AI takeup by all the various sectors and harness its full potential we need to improve machine-learning algorithms without thereby undermining data confidentiality. Striking the right balance between privacy and data use is no longer a pipedream. The technology multinational GMV has just presented uTile PET (Privacy-Enhancing Technologies): a solution capable of carrying out secure private calculations on distributed data, without exposing it or moving it from organizations.

uTile harnesses confidential data in order to improve machine learning algorithms and analytical models, complying at all times with organizational remits, data-privacy obligations and current law. José Carlos Baquero, Artificial intelligence and Big Data manager of GMV’s Secure e-Solutions sector argues as follows: «With uTile no we no longer have to choose between data privacy and data harnessing. Advanced cryptographic methods keep the data encrypted while all necessary calculations are made. uTile hence guarantees that organizations’ sensitive data is never exposed or transferred through departments, organizations or countries. Furthermore, data subjects do not even have to entrust their data to third parties. This data always remains protected between the organization’s own internal controls, whether on-premise or in the cloud, and the sensitive information remains private throughout the whole calculation process».

Data containing sensitive and private information is now being generated in a growing stream. On the other hand, organizations are increasingly using advanced analytics whose algorithms use data, of good or poor quality, with their security and privacy restrictions, etc, and data scientists are often hard put to access this information.

According to the World Economic Forum’s report “Transforming Paradigms: A Global AI in Financial Services Survey”, published on 29 February 2020, data-protection and -sharing constraints are usually the main hindrance to AI takeup. Under the overarching program of Spain’s Ministry of Economics and Digital Technology Enabling firms (Ministerio de Economía y Empresa de Tecnologías Habilitadoras Digitales), GMV is now developing the use case involving comparison of clinical treatment efficiency, in which hospitals, clinics, research centers and the pharmaceutical industry need to cross check healthcare results in the interests of drawing better treatment-efficiency conclusions. Patient data, however, enjoys special protection under GDPR, complemented in Spain by the Patient Autonomy Law (Ley de Autonomía del Paciente). With uTile we can share clinical-treatment information as useful as survival rates, the value of biomarkers, prognoses, the mean age of patients, etc.

In short, all organizations can benefit from uTile (which strikes the right balance between data-privacy and -use), by sharing and even monetizing in a secure way the data-based knowledge, thanks to encrypted computing, complying with distributed data source privacy and facilitating secure information exchange.