A New Wave Towards Trust

About

BACKGROUND The future of city life is an abundance of monitoring activities and computational capabilities supplied by sensors and devices around the people living in cities. Given the wealth of data, trust is a key factor to ensure privacy and security so that people can engage with the technological future of city life. OUR SOLUTION Our solution addresses the fundamental issue of trust between an individual and a device, and between individuals and organisations. City life places users in a succession of situations requiring different levels of data sharing and device accessibility, e.g. as they commute between home, work and leisure activities. We automatically understand the levels of trust derived for use within different situations to empower a sustainable future smart city ecosystem. Our service leverages smartphones and wearables to gather information from real-world data from users during their daily situations. The collected information is analysed in order to build trust profiles among the users. These trust profiles are built based on behavioural traits extracted from psychology and contextual information from social interactions, cultivating a top-down approach. The collected information is stored in the user’s device and is at the user’s disposal, in order to respect users’ privacy. After users’ consent, these trust profiles are exposed to potential customers, allowing them to understand how trustworthy is the peer with whom they will perform a transaction. As this technology is based on various spontaneous behavioural traits of the user, the gap for manipulation of user’s trustworthiness is minimised. The potential customers can easily deploy our service and provide it to their end-users through online application stores like App Store and Google Play.   Existing solutions rely heavily on on-line social networks interactions (e.g. TrustCloud, Legit etc.). This requires explicit user engagement, provides very subjective and erroneous trust estimations, since users in many cases wildly exaggerate their feedback regarding a corresponding peer. On the contrary, our solution relies on actual, real-life encounters and behavioural traits to understand the levels of trust. In the case a peer wants to join an online service such as eBay and AirBnB, a large obstacle is faced as there is no trust level for the particular peer. Our service, is able to estimate the level of trust based on the peer’s relations who have already established a certain level of trustworthiness during their daily encounters. This allows the peer to immediately enter the network.  Furthermore, our service detects generalised trust from face-to-face interactions which could be utilised from different organisations to initially establish the level of trust among the users. Depending on the context of the particular organisation, the generalised trust provided by our service could be narrowed to the specific context. To our knowledge, this is the first solution that infers generalised trust based on real-world social encounters.   As stated by multiple experts of trust in the sharing economy including Rachel Botsman, having one universal trust measurement is not reliable. However, trust information derived from a particular context could provide valuable information for other types of context. For example, in the sharing economy a person that performs all the tasks on time at TaskRabbit, has high probability of being on time when he is about to meet the customer from AirBnB. Being on time for a task or meeting is a universal trust characteristic of a person that could be derived from different contexts (bottom-up approach). To our knowledge, there is no solution that is consolidating trust information from various platforms to derive some universal trust characteristics among the peers.   The system consists of a mobile app for smartphones and wearables that senses data from the user and understands the characteristics of the social encounters, and exposes the trust profiles to the potential customer in a secure and privacy-preserving way.   The mobile app will perform the data sensing and understanding of users’ behavioural traits. Currently, we have developed a social-encounters and behavioural-traits tool for smartphones. In order to consider also the interactions a person has with other people through the device, phone calls, messaging and contacts are logged. A novel trust model has been developed that aggregates the various data streams and provides generalised trust. Upon request, the mobile device will provide the trust information through an application program interface (API). Currently, the app has been developed for the Android mobile application platform.   Depending on whether the potential customers are individuals or organisations, they will interact with the service in a different way. Individuals will use this service in order to ensure their privacy and security but also benefit from third-party services. By downloading a mobile app and providing clear consent regarding the data collected and exposed, a user will no longer need to interfere with the process except for the cases where unwanted information may be deleted. Organisations will interact with the service through an API to retrieve the trust profiles to ensure users’ data are not vulnerable. Our service is a novel, easy to deploy, scalable way to understand the trust relationships among people while minimising the gap for manipulation as it is based on behavioural traits from real-world encounters.   TARGET MARKETS The major customer segments of the business model are government and large organisations, application providers including health-case, corporate and academic institutions as well as sharing economy related companies. Trust is a crucial factor for today’s and future economy based on which novel and innovative business models are built on. Given this, our service can integrate with mobile apps with hundreds of millions users worldwide, resulting in a global innovation and improving a concept in large-scale. An example of the scalability is PollFish (https://www.pollfish.com/) that provides a service for apps, which integrates with existing apps in order to survey users reaching a market size of 160 million users. Our services could integrate with businesses in the sharing economy such as AirBnB, Uber and TaskRabbit reaching over 60 million users. Health-care: Deploying the application through government networks for health and social care, allows people to live independently in large cities. Unlike other technologies that monitor people in their own homes and provide alerts in the case an emergency, this technology integrates with other aspects of the physical environment. For example, if a vulnerable person falls in their home, the door access would be synchronised with the device of a trusted neighbour so that they could gain access to help the vulnerable person. Corporate: A company with a mobile workforce for example in city infrastructure maintenance may deploy the service. In that sense, companies are able to configure the access to data available to their mobile workforce depending on their location and activities. For example, if a company employee is meeting with a trusted supplier, access to data would be available to the employee and the supplier during the meeting. However, for a meeting with a new potential supplier, different data would be available to the employee during that meeting. Individual: An individual private user may download and deploy the mobile app to ensure privacy on the personal data of the device. The device self-configures based on whether the user is in a trusted or a non-environment for example to block connection to other devices in public and to share data with family members when at home. The user is able to engage in configuration and then trust the device to adapt to the context the user is in. Sharing economy: Deploying the application in such an organisation, will allow the creation of a trust network among the peers. Unlike other technologies, which generate their trust profiles based on solely the users’ feedback, this technology considers also the users’ behavioural traits in daily situations. This constitutes a robust security measurement that minimises the manipulation of users’ trust profiles in a seamless manner as it does not introduce any additional burden on the users. For example, a user is joining a sharing economy platform such as AirBnB for the first time and wants to provide a let a room. However, as the user has just joined the platform there is no trust rating available for him. So, our mobile app would allow such a user to receive a temporary trust rate based on his everyday behaviour and trustworthy “friends” that are members of the platform and have already established a trust rating. In that sense the user will be able to join the platform and directly start to trade without needing a bootstrap phase to establish a trust ranking.