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Anonymisera

Anonymisera is a term used in data protection and privacy contexts to describe the process of removing or altering personal identifiers from data so that individuals cannot be identified. The concept encompasses both complete anonymization, where re-identification is not reasonably possible, and partial forms such as pseudonymization or masking that reduce identifiability while preserving some data utility.

Common methods associated with anonymization include removing direct identifiers (names, addresses, social security numbers), replacing them

Anonymization is widely used in research, statistics, healthcare, government data releases, and journalism to balance the

See also: anonymization, pseudonymization, differential privacy.

with
pseudonyms,
applying
data
masking,
and
using
techniques
like
generalization,
suppression,
or
data
synthesis.
More
advanced
approaches
aim
to
provide
formal
privacy
guarantees,
such
as
differential
privacy,
or
to
ensure
data
satisfy
criteria
like
k-anonymity,
l-diversity,
or
t-closeness,
which
reduce
the
risk
of
linking
records
to
individuals.
value
of
data
with
individual
privacy.
It
is
influenced
by
legal
frameworks
such
as
data
protection
laws,
which
encourage
or
require
minimization
and,
in
some
cases,
mandate
documented
anonymization
practices.
Importantly,
true
anonymization
aims
to
render
re-identification
impractical,
but
experts
warn
that
certain
combinations
of
data
and
external
information
can
still
threaten
anonymity,
so
risk
assessments
and
governance
are
critical
components
of
the
process.