SmartPrivacy for the Smart Grid – Embedding Privacy into the Design of Electricity Conservation a report by Ann Cavoukian, PhD Information and Privacy Commissioner of Ontario, Canada


The current electrical grid is seen by many as the greatest engineering achievement of the 20th century. Indeed, it is considered to be the largest machine on the planet. However, it is becoming increasingly out-of-date and overburdened. Efforts to modernise the grid, making it smarter, stronger and greener, are gathering momentum, especially in North America and Europe.


Smart Grid technologies will produce significant efficiencies in both cost and energy throughout the electrical distribution system, providing greater consumer control. We all stand to benefit from the Smart Grid, but we will benefit most if the Smart Grid is well-designed and well- implemented. An essential component of this will be making sure that the Smart Grid is ‘smart’ about privacy.


Consumers represent almost one-quarter of all of the energy consumed across the globe.1 the most private of all places.2


That energy is consumed in the home – The infrastructure that will support the


future Smart Grid will be capable of capturing far more detailed information about energy use, even at the appliance level, than has ever been available before.3


For consumers, this amounts to extensive


data collection reflecting the habits and activities that take place within the walls of their most private spaces. This has been described as building a ‘library’ of personal information.


Many other sectors have had the opportunity, particularly over the past 20 years, to learn how important good privacy practices are to maintaining good business practices. There was a time when privacy was seen as a regulatory imperative – another requirement of the state to be managed. However, times have changed.4


identified, even when a minimum of non-personal information about them is available.6


The Smart Grid will include several end-user components and activities that are likely to increase the collection, use and disclosure of personal information by utility providers, and also possibly by third parties. These components may include smart meters, smart appliances, dynamic pricing, tracking tools and software applications that monitor consumption, and load management technologies that grant utilities active or passive control over smart appliances.


Through smart meters alone – which are just one component of the Smart Grid already being rolled out in many markets – highly detailed information will soon be collected about millions of households. As a result, electricity usage profiles could become a source of behavioural information at a granular level.7


It will not take long to turn this


information into an understanding of the personal habits, behaviours and lifestyles of the individuals residing within each home, such as:8


• •


• •


• • In an increasingly


‘smart’ networked world, the successful players are those who understand and respond to privacy as a trust issue and work hard to earn and maintain the trust of consumers.


Personal Information in the Smart Grid


Privacy relates essentially to control – maintaining control over our personal information. The term ‘personal information’ refers to any recorded information about an identifiable individual. In addition to name and contact and biographical information, it can include information about individual preferences, transactional history, records of activities or travels or any information derived from these, such as a profile or score. In the context of the Smart Grid, energy usage information that is linked to personal identifiers constitutes personal information.


Even data that are anonymised can raise privacy issues. In behavioural advertising, for example, users can be treated differently or marketed to specifically based on individual but not personally linked data, raising the need for enhanced privacy protection.5


In addition, researchers have documented the ease with which users may become © TOUCH BRIEFINGS 2010 109


These kinds of details may later be combined with other information, such as work location and hours, to generate further data and arrive at assumptions that might be of interest to marketers, insurance adjustors, would-be thieves, employers and others. For example, one could extrapolate from energy data that a homeowner tends to arrive home shortly after the bars close, that he/she is a restless sleeper and is sleep-deprived, that he/she leaves late for work and often leaves appliances on, that exercise equipment is not used, that his/her laundry is washed infrequently and any children are left at home alone.8


Ann Cavoukian is Information and Privacy Commissioner of Ontario, Canada. She is recognised as one of the leading privacy experts in the world. Noted for her seminal work on privacy-enhancing technologies in 1995, her concept of ‘privacy by design’ seeks to embed privacy into the design specifications of technology, thereby achieving the strongest protection possible.


E: commissioner@ipcion.ca the times of day when the house is usually empty;


whether the house has an alarm system and, if so, how often it is activated;


how often and for how long televisions and computers are on;


whether occupants sleep through the night or wake often (turning on lights, etc.);


whether the occupants have a tendency to leave appliances on when they are out (e.g. irons or stove tops);


whether and how often exercise equipment is used.


Transmission & Distribution


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