Abstract:
The invention relates to a method computing a probabilistic encryption scheme for encrypting a data item in an electronic device comprising the steps of: computing a plurality of random bit strings in a computation cluster; sending the computed plurality of random strings to the electronic device; generating a random string (r E ) for using in the encryption scheme in the electronic device using a subset of the plurality of the random strings computed in the computation cluster and encrypting the data item using the random string computed in the electronic device. The present invention also relates to a corresponding system and corresponding computer program product comprising one or more computer readable media having computer executable instructions for performing the steps of the method.
Abstract:
The invention relates to a method for aggregation of a performance indicator of a device comprising the steps of: concatenating a respective first data item to a plurality of second data items in the device; encrypting the plurality of concatenated second data items relevant for computing the performance indicator using a first encryption key in the device, wherein the first encryption key is based on an additive homomorphic encryption scheme; sending the encrypted concatenated second data items to a computation cluster; computing the performance indicator on the computation cluster using the encrypted concatenated second data items and computing an aggregate value regarding the performance indicator by summing up the encrypted concatenated second data items; sending the aggregate value to a server of a service provider of the device; decrypting the aggregate value using a second encryption key on the server of the service provider; and verifying the decrypted result by checking whether the decrypted sum computed by summing up the encrypted concatenated second data items comprises a predetermined value. The present invention also relates to a corresponding system and corresponding computer program product comprising one or more computer readable media having computer executable instructions for performing the steps of the method.
Abstract:
A method of automatically generating secure code (12, 26) comprises: receiving source code (22) and security constraints (24) for the source code (22), the security constraints (24) encoding, to what extent a variable (44) in the source code (22) is considered secure; and generating secure code (12, 26) from the source code (22) and the security constraints (24) by replacing non-secure operations (46) in the source code (22), which operate on the variables (44) considered as secure, with secure operations (46a); wherein a secure operation (46a) is an operation, which, when applied to at least one encrypted variable (44), generates an encrypted result, which, when decrypted, is the result of the non-secure operation (46) applied to the not encrypted variable (44).
Abstract:
The present invention discloses a method for computing a secret value comprising a first secret using a function including an operation, comprising: computing, by a host, a first encrypted value of the first secret with a first key; sending, by the host, the first encrypted value to a value holder and the first key to a key holder, wherein the value holder and the key holder are independently trusted by the host; computing, by the value holder, a computed encrypted value from the first encrypted value using the function; and computing, by the key holder, a computed key from the first key using the function.
Abstract:
The invention relates to a method for storing data blocks from client devices (1, 4) to a cloud storage system (3), the method comprising the steps of: d) storing an encrypted first data block (2) and a challenge of the first data block (2) of a first client device (1) on the cloud storage system (3), e) determining if a hash of a second data block (5) of a second client device (4) stored on the cloud storage system (3) equals the hash of the first data block (2), f) if yes, transmitting the challenge of the first data block (2) from the cloud storage system (3) to the second client device (4), g) extracting, at the second client device (4), the bits at the positions or at the range contained in the challenge, hashing the extracted bits, encrypting the hashed bits with a public key of the first client device (1) or of the second client device (4) and uploading the encrypted bits from the second client device (4) to the cloud storage system (3), and h) storing the encrypted bits from the second client device (4) on the cloud storage system (3).
Abstract:
The present invention generally relates to a context-aware security self-assessment method or module that determines the context in which the device is used and based on this, assesses the devices security settings. The context may refer to the system environment, the applications the device is used for, and/or the current life-cycle stage of the device, without being limited to said contexts. The method of the present invention preferably prioritizes and rates the security relevant findings and presents them in combination with mitigation options through a web interface, a configuration tool, or through notifications in the control system.
Abstract:
A method for evaluating data (28) is based on a computational model, the computational model comprising model data (26), a training function and a prediction function. The method comprises training the computational model by: receiving training data (22) and training result data (24) for training the computational model, and computing the model data (26) from the training data (22) and the training result data (24) with the training function. The method comprises predicting result data (30) by: receiving field data (28) for predicting result data (30); and computing the result data (30) from the field data (28) and the model data (26) with the prediction function. The training data (22) may be plaintext and the training result data (24) may be encrypted with a homomorphic encryption algorithm, wherein the model data (26) may be computed in encrypted form from the training data (22) and the encrypted training result data (24) with the training function. The field data (28) may be plaintext, wherein the result data (30) may be computed in encrypted form from the field data (28) and the encrypted model data (26) with the prediction function.