Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (θ) to the Shannon entropy estimate, to determine a value for the argument parameter (θ) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (θ).

Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (θ) to the Shannon entropy estimate, to determine a value for the argument parameter (θ) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (θ).

A secure computation apparatus (1.sub.i) generates a concealed value [r] of a random number r following a discrete Laplace distribution with parameter α. A bit stream generating unit (11) generates a concealed value stream [b.sub.0], [b.sub.1], . . . , [b.sub.N] that is constituted by a concealed value [b.sub.0] of a random number bit bo following a Bernoulli distribution with probability (1−α)/(1+α) and concealed values [b.sub.1], . . . , [b.sub.N] of random number bits b.sub.1, . . . , b.sub.N each following a Bernoulli distribution with probability (1−α). An absolute value determining unit (12) obtains a concealed value [L] of a position L at which 1 is first set from the head of the random number bits b.sub.0, b.sub.1, . . . , b.sub.N. A sign determining unit (13) obtains a result [L.Math.s] obtained by multiplying the concealed value [L] by a concealed value [s] of a random sign s, as a concealed value [r] of the random number r.

A secure computation apparatus (1.sub.i) generates a concealed value [r] of a random number r following a discrete Laplace distribution with parameter α. A bit stream generating unit (11) generates a concealed value stream [b.sub.0], [b.sub.1], . . . , [b.sub.N] that is constituted by a concealed value [b.sub.0] of a random number bit bo following a Bernoulli distribution with probability (1−α)/(1+α) and concealed values [b.sub.1], . . . , [b.sub.N] of random number bits b.sub.1, . . . , b.sub.N each following a Bernoulli distribution with probability (1−α). An absolute value determining unit (12) obtains a concealed value [L] of a position L at which 1 is first set from the head of the random number bits b.sub.0, b.sub.1, . . . , b.sub.N. A sign determining unit (13) obtains a result [L.Math.s] obtained by multiplying the concealed value [L] by a concealed value [s] of a random sign s, as a concealed value [r] of the random number r.

A system and method of generating a one-way function and thereby producing a random-value stream. Steps include: providing a plurality of memory cells addressed according to a domain value wherein any given domain value maps to all possible range values; generating a random domain value associated with one of the memory cells; reading a data value associated with the generated random domain value; generating dynamically enhanced data by providing an additional quantity of data; removing suspected non-random portions thereby creating source data; validating the source data according to a minimum randomness requirement, thereby creating a validated source data; and integrating the validated source data with the memory cell locations using a random edit process that is a masking, a displacement-in-time, a chaos engine, an XOR, an overwrite, an expand, a remove, a control plane, or an address plane module. The expand module inserts a noise chunk.

A system and method of generating a one-way function and thereby producing a random-value stream. Steps include: providing a plurality of memory cells addressed according to a domain value wherein any given domain value maps to all possible range values; generating a random domain value associated with one of the memory cells; reading a data value associated with the generated random domain value; generating dynamically enhanced data by providing an additional quantity of data; removing suspected non-random portions thereby creating source data; validating the source data according to a minimum randomness requirement, thereby creating a validated source data; and integrating the validated source data with the memory cell locations using a random edit process that is a masking, a displacement-in-time, a chaos engine, an XOR, an overwrite, an expand, a remove, a control plane, or an address plane module. The expand module inserts a noise chunk.

A method for providing random numbers for control units communicating via a vehicle network, in which a random number generator having an aggregation component, a storage unit and a distribution component is provided. A plurality of control units each with at least one entropy source are formed. Their raw data are transmitted to the aggregation component via the vehicle network. A quality assurance of the combined raw data from the entropy sources is carried out using only those combined raw data which both occur in a non-deterministic manner and contain a minimum degree of entropy as qualified raw data. The qualified raw data are converted into an aggregated data block by a cryptographic one-way function and securely stored as a random number in the storage unit. The random number stored in the storage unit is transmitted to a control unit via the vehicle network by the distribution component.

A method for providing random numbers for control units communicating via a vehicle network, in which a random number generator having an aggregation component, a storage unit and a distribution component is provided. A plurality of control units each with at least one entropy source are formed. Their raw data are transmitted to the aggregation component via the vehicle network. A quality assurance of the combined raw data from the entropy sources is carried out using only those combined raw data which both occur in a non-deterministic manner and contain a minimum degree of entropy as qualified raw data. The qualified raw data are converted into an aggregated data block by a cryptographic one-way function and securely stored as a random number in the storage unit. The random number stored in the storage unit is transmitted to a control unit via the vehicle network by the distribution component.

Provided is an authentication device capable of generating a master key suited to a UE in a 5GS. The authentication device (10) includes a communication unit (11) configured to, in registration processing of user equipment (UE), acquire UE key derivation function (KDF) capabilities indicating a pseudo random function supported by the UE, a selection unit (12) configured to select a pseudo random function used for generation of a master key related to the UE by use of the UE KDF capabilities, and a key generation unit (13) configured to generate a master key related to the UE by use of the selected pseudo random function.

Systems and methods are provided for detecting anomalous messages on a multipoint serial communications bus by extracting features from a first and a second message, including a time delay between the first and the second messages and, for each message, a sender address, a recipient address, a bus number, and a word count. A message transition pattern including the extracted features is generated. A probability of occurrence of the message transition pattern is determined by comparing the message transition pattern to a pattern dictionary, and the second message is determined to be anomalous when the probability is less than a predetermined threshold.