伊人直播

Data we collect

The Department for Education (DfE) and its executive agencies have legal powers to collect data about individuals in the children鈥檚 services, education, apprenticeships, and wider skills training sectors, in England.

This data forms a significant part of our evidence base.

We use it:

• in find and compare schools in England
• in adoption scorecards
• in statistical releases
• to evaluate and inform educational policy
• to assess funding for local authorities and schools
• to assess the success of the apprenticeship scheme
• to consider workforce salaries and pay increases

Our personal information charter details the standards we follow when we collect personal data.

Why we share data

DfE and its executive agencies are responsible for ensuring the provision of education and children鈥檚 services is effective and efficient. The following set of principles help us to approve or reject any project proposals which involve sharing personal level data with external organisations.

DfE shares personal data:

• where there is a clear benefit to the education or children鈥檚 services sector
• to inform debate
• which is able to benefit a sizeable section of the target sector and is not solely for commercial gain
• to encourage the research community to work collaboratively with the department and build the evidence base together - where the research is likely to have a significant impact, DfE will ensure third parties use appropriate methodologies and make good use of peer review
• where the project is aligned to DfE鈥檚 research areas of interest
• for secondary research, where:
  • it is commissioned, funded, sponsored or supported by DfE or the wider education and children鈥檚 services sector
  • it drives behaviour which is consistent with DfE policy
  • the output does not duplicate , publications or other services offered by DfE

Who we share data with

You can search and find DfE external data shares of all ongoing personal level data sharing delivered through data sharing agreements, including an update on police, Home Office and Family Court Order use of limited parts of our data when they have clear evidence of criminal activity.

How we share data

DfE and its executive agencies will ensure that any projects that are permitted to work with our data are fully compliant with and are subject to the 5 safes framework. Together, we ensure that safe people access our safe data for safe projects in safe settings to produce safe outputs.

DfE will only share data with a third party where we have a lawful basis for the data share. That lawful basis will be based on the specifics of each data request and on the personal data they are seeking to use. For example, DfE may use article 6(1)(e) 鈥榩ublic task鈥� as the lawful basis where the task or function has a clear basis in law.

The following are some examples of legal powers we have used to share personal data which support the use of public task.

allows us to share pupils鈥� personal data with certain third parties, including:

• schools
• local authorities
• researchers
• organisations connected with promoting the education or wellbeing of children in England
• organisations fighting or identifying crime
• other specified crown and public bodies

The permit the sharing of individual child information from early years providers with persons who are conducting research into the educational achievements of children.

The permit the sharing of data about children in alternative provision with persons who are conducting research into the educational achievements of children.

The permits the sharing of learner data to enable or facilitate the exercise of any function of the DfE relating to education or training.

The covers the sharing of learner data in connection with the exercise of an assessment function defined as:

• evaluating the effectiveness of training or education
• assessing policy in relation to the provision of training or education
• assessing policy in relation to social security or employment as it affects the provision of or participation training or education

The permits the sharing of a subset of data for learners in further education data with persons who, for the purpose of promoting the education or well-being of students in England, are conducting research or analysis, producing statistics, or providing information, advice or guidance.

The covers the sharing of children鈥檚 services data to assist other persons in conducting research into any matter connected with a number of specified functions of the department or local authorities.

, permits the sharing of data with persons conducting research relating to qualifying workers or qualifying trainees which may be expected to be of public benefit.

The permits the sharing of Universities and Colleges Admissions Service (UCAS) data to approved persons doing:

• research into the choices available to individuals who are either:
  • applying for admission to higher education courses provided by English higher education providers
  • considering whether to accept an offer for admission on such a course from such a provider
• research into equality of opportunity
• research areas approved by the Secretary of State

Chapter 5 of Part 5 of the facilitates the linking and sharing of de-identified data by public authorities for accredited research purposes in the public good. It is designed to support the UK research community, both within government and beyond. Currently, DfE shares LEO data using this lawful basis through Office for National Statistics (ONS) governance, .

Vision for sharing data

DfE鈥檚 vision for sharing personal data with external organisations is two-fold.

1. Where data can be shared under DEA, project approval will be managed through the (RAS).
2. Where data cannot be shared under DEA, project approval will be managed by the DfE data sharing service under DfE legislation.

DfE will only share personal data under DEA which has already been de-identified data by ONS as service provider and DEA accredited processor for disclosure.

You can find out more about how the . All research projects under DEA are consistently accredited using the Research Code of Practice and Accreditation Criteria which was approved by the UK Parliament in July 2018. As the statutory accrediting body, the has also established a to oversee the independent accreditation of processors, researchers and research projects.

Some linked DfE datasets are also available through the UK Data Service. Information about applying for DfE personal data is available.

Five safes

All DfE data, whether accessed via ONS RAS or the DfE data sharing service, will be subject to the 5 safes:

• safe settings
• safe people
• safe projects
• safe outputs
• safe data framework for how we protect data

Safe settings

Our default route for sharing personal data for research purposes is through the ONS Secure Research Service (SRS) or Integrated Data Service (IDS) physical and virtual datalabs (including remote access). This is a safer way to access data compared with the transfer of data files to individual organisations.

It鈥檚 not always suitable to get data through the SRS or IDS. If you鈥檙e receiving data directly from us, we make sure that data is only provided to your organisation and held in a safe settings by checking:

• your organisation鈥檚 IT and building security
• you don鈥檛 keep the data for longer than allowed

Safe people

We only share our data with people we trust to use it safely and responsibly.

To access personal data via聽ONS SRS or IDS, you have to:

• be approved by us
• sign an individual declaration form to confirm that you abide by our data sharing agreement
• complete the聽

To receive personal data directly from us, you have to:

• provide evidence that your organisation applies appropriate vetting of individuals intending to use government data
• sign an individual declaration form to confirm that you abide by our data sharing agreements
• complete recognised data protection and information security training

Safe projects

We have a senior board, the data sharing approval panel (DSAP), which makes sure all external requests for personal data meet our data sharing principles and are:

• legal
• ethical
• proportionate
• secure

The board includes senior internal and external data experts who meet regularly to consider cases and approve or reject requests.

See (PDF, 209 KB, 10 pages) for more information.

Safe outputs

When applying to receive our data, you have to:

• make it clear how you intend to use the data
• follow the relevant agreement and schedule for the data share

When working through the SRS or IDS, if you want to use the results from your analysis outside of the service these will be checked by ONS. They鈥檒l make sure the outputs protect data confidentiality and can鈥檛 be used to identify any specific individuals or organisations.

Safe data

We now classify all persona data leaving us against 2 criteria:

• the risk that an individual could be identified
• how sensitive the data item is

This makes it easier for us to be transparent about:

• what kind of data we share with third parties
• our decision making

Safe data classification framework

When applications for personal data are made, we use these classifications to scrutinise the data request to make sure that:

• we only share data proportionate to the intended purpose
• we are comfortable with the level of protection around the individual鈥檚 identity that is built within the dataset we are allowing the third party to access

We also use these classifications for checking the additional conditions of processing which is a legal requirement.

We publish the risk of identification and sensitivities in the DfE external data shares.

Assessing the risk of identification

We use 6 levels of identification risk to describe data.

Level 1: instant identifiers

Examples of personal level data that instantly identify an individual within a dataset include:

• full names
• full addresses
• email addresses
• phone numbers
• IP addresses

Level 2: meaningful identifiers

These are identifiers that are assigned to people such as a:

• NHS number
• national insurance number

In education, pupils have identifiers such as:

• unique pupil numbers
• unique learner numbers
• national candidate numbers

We call these meaningful identifiers because they:

• directly identify the individual
• are often known by the individual
• can easily be used to link other educational data

A meaningful identifier could be combined with other data, increasing the chance of identification.

Where possible, we鈥檒l:

• avoid sharing instant or meaningful identifiers
• aim to limit data-sharing to data with a risk of identification set at level 3 or below

If there鈥檚 a need to identify an individual, we鈥檒l ensure that:

• it鈥檚 justified
• it鈥檚 proportionate to the intended purpose
• we build an adequate level of protection into each instance of data-sharing

The classification of all data extracts with risk of identification level 1 or 2, will be published as 鈥榠dentifiable personal level data鈥�.

Level 3: meaningless identifiers

A lot of research is interested in how individual pupils progress over time. To achieve this whilst safeguarding the individual鈥檚 identity, we make use of identifiers that have no meaning outside of our data.

These are less risky than meaningful identifiers as they can鈥檛 be used to join our data to non-DfE data.

Level 4: non-identifiers with higher identification risk

Within our personal level data, there are data variables that do not fall into level 1, 2 or 3 but can still be joined together to identify individuals.

Even if the names, addresses, meaningful reference numbers have all been taken out of the data we know there is still a risk that certain variables could result in an individual being identified. This is what we class as 鈥榬e-identification risk鈥�.

Assessing re-identification risk is not an exact science. We鈥檝e consulted experts in the field and have found that certain combinations are more risky than others. For example the risk increases if we include:

• number of siblings
• the school a child attends
• postcode of home address

We identify these combinations within the data requested and then question whether they are essential to the project purpose or research.

Level 5: non-identifiers with lower identification risk

This is the level of identification risk we give to data variables that do not meet any of the above criteria.

The classification of all data extracts with risk of identification level 3, 4 or 5, will be published as 鈥榙e-identified personal level data (with re-identification risk)鈥�.

Level 6: aggregate or suppressed data

We use these terms to describe the method of aggregating data. These data shares do not come to DSAP.

Where there are small numbers of individuals within the aggregated data, the appropriate levels of suppression are applied to make sure there is only an extremely remote risk of identification.

Assessing the sensitivity of data

We use 5 categories to describe the sensitivity of data.

A. Public commitment that this data will never leave the department

There are a few data variables that we have publicly committed will only be used for internal departmental purposes. This category is used to make sure that those commitments are embedded into all data governance processes.

Any request including sensitivity A data would be rejected by DSAP.

B. Highly sensitive data about interactions with Children鈥檚 services.

We collect data about the interactions some children have with children鈥檚 services, such as being:

• fostered
• looked after
• adopted

We consider this as highly sensitive. Sharing this data for research purposes (using appropriate levels of data safeguarding) helps us to understand more about the children鈥檚 experience of these interventions to improve children鈥檚 services outcomes.

Sensitivity B data undergoes an additional level of scrutiny by the children鈥檚 services teams on top of DSAP scrutiny.

C. Sensitive data not captured as a special category under GDPR

The law defines areas of personal data that are particularly sensitive for individuals as 鈥榮pecial categories鈥�.

Within education, we believe that there are variables that citizens would treat as equally sensitive, but are not covered in GDPR, such as free school meal eligibility.

We use this category to make sure such variables are thought about in the same way as GDPR special category data during our decision-making processes, even if legally there are differences.

Sensitivity C data will undergo the same level of scrutiny as if they were sensitivity D data.

D. Sensitive data captured as a special category under GDPR

GDPR special categories are clearly set out in law. Most relevant in the context of education data are:

• ethnicity
• disability
• elements of special educational need (SEN) that have a health context

Sensitivity D data requests require additional conditions of processing to be justified, as set out in law, before DSAP can consider it for data sharing.

E. Other

Data that does not fit into any of the other 4 categories, such as exam results.