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Information flow in an information theoretical context is the transfer of information from a variable to a variable in a given process. Not all flows may be desirable; for example, a system should not leak any confidential information (partially or not) to public observers—as it is a violation of privacy on an individual level, or might cause major loss on a corporate level.

Securing the data manipulated by computing systems has been a challenge in the past years. Several methods to limit the information disclosure exist today, such as access control lists, firewalls, and cryptography. However, although these methods do impose limits on the information that is released by a system, they provide no guarantees about information propagation. For example, access control lists of file systems prevent unauthorized file access, but they do not control how the data is used afterwards. Similarly, cryptography provides a means to exchange information privately across a non secure channel, but no
guarantees about the confidentiality of the data are given once it is decrypted. In low level information flow analysis, each variable is usually assigned a security level. The basic model comprises two distinct levels: low and high, meaning, respectively, publicly observable information, and secret information. To ensure confidentiality, flowing information from high to low variables should not be allowed. On the other hand, to ensure integrity, flows to high variables should be restricted. For example, considering two security levels and (low and high), if , flows from to , from to , and to would be allowed, while flows from to would not. Throughout this article, the following notation is used:
variable (low) shall denote a publicly observable variable
variable (high) shall denote a secret variable

Where and are the only two security levels in the lattice being considered.

Non interference is a policy that enforces that an attacker should not be able to distinguish two computations from their outputs if they only vary in their secret inputs. However, this policy is too strict to be usable in realistic programs. or they ignore updates that might leak information. A prominent way to enforce information flow policies in a program is through a security type system: that is, a type system that enforces security properties. In such a sound type system, if a program type checks, it meets the flow policy and therefore contains no improper information flows.

A when declassification policy regulates when the information can be released. Policies of this kind can be used to verify programs that implement, for example, controlled release of secret information after payment, or encrypted secrets which should not be released in a certain time given polynomial computational power.

An implicit flow occurs when code whose conditional execution is based on private information updates a public variable. This is especially problematic when multiple executions are considered since an attacker could leverage the public variable to infer private information by observing how its value changes over time or with the input.

Permissive upgrade introduces an extra security class P which will identify information leaking variables. When a High variable affects the value of a Low variable, the latter is labeled P. If a P labeled variable affects a Low variable the program would be halted. To prevent the halting the Low and P variables should be converted to High using a privatization function to ensure no information leakage can occur. On subsequent instances the program will run without interruption.

Privatization inference extends permissive upgrade to automatically apply the privatization function to any variable that might leak information. This method should be used during testing where it will convert most variables. Once the program moves into production the permissive upgrade should be used to halt the program in case of an information leakage and the privatization functions can be updated to prevent subsequent leaks.

Beyond applications to programming language, information flow control theories have been applied to operating systems, distributed systems, and cloud computing.