Abstract: A device includes a backup activator and a backup generator. The backup activator decrypts an encrypted concealed snapshot of a user's secret using a protection key, producing a concealed snapshot, and reconstructs a snapshot from the concealed snapshot, the user's password and a multiparty secret reconstruction system. The backup generator conceals the snapshot with a password of the user and the multiparty secret protection system, generates the protection key and encrypts the concealed snapshot with the protection key. The backup activator includes a helper based key recovery unit which sends encrypted portions of the protection key, each portion being associated with and decryptable by a helper, and which combines decrypted portions into the protection key. The backup generator includes a helper based key encrypter to split the protection key into at least one portion per an associated helper and to encrypt each portion with the associated helper's public key.

Abstract: A secret protection method operative on a user's device with a group of servers includes concealing a secret on the user's device with the user's password, the concealing not involving any of the servers, storing the output of the concealing on the device and reconstructing the secret from a newly received password and the stored output in conjunction with a subset of the group of servers, wherein the reconstructing is minimally affected by intermittent network connection problems and attacks that interfere with the communication between the user's device and the subset of servers, The reconstructing includes performing an enhanced PPSS (Password Protected Secret Sharing) reconstruction protocol between the user's device and the subset of servers and receiving the results of throttling performed by each of the subset of servers.

Abstract: Engines, systems and methods are disclosed for capturing inputs from an existing or prospective small business owner through an Internet-based graphical user interface, gathering small business and consumer credit data from external sources, combining with data sourced from a database of lender preferences specifying particular loan application characteristics and delivering an output of information, educational lessons, loan qualification offers, and a heuristic assessment of funding likelihood.

Abstract: Engines, systems and methods are disclosed for capturing inputs from an existing or prospective small business owner through an Internet-based graphical user interface, gathering small business and consumer credit data from external sources, combining with data sourced from a database of lender preferences specifying particular loan application characteristics and delivering an output of information, educational lessons, loan qualification offers, and a heuristic assessment of funding likelihood.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for detecting abnormal behavior. Detecting abnormal behavior can include receiving a mean at a previous time interval, a sum of squares at the previous time interval, and a first sample of a metric at a current time interval from a system and adjusting a first weight and a second weight at the current time interval to the first sample and a system change report. Detecting abnormal behavior can also include calculating a mean and a standard deviation of the metric at the current time interval by assigning the first sample the adjusted first weight and by assigning the mean and the sum of squares at a previous time interval the adjusted second weight and detecting abnormal behavior by comparing the first sample to an outlier value based on the mean and the standard deviation at the previous time interval.

Abstract: To determine a seasonal effect in temporal data, for each of a plurality of candidate seasons, the following is performed. An interval is defined for the corresponding candidate season. The interval is divided into plural buckets containing respective sets of the temporal data, and statistical measures for corresponding buckets are computed. The computed statistical measures are used to compute a score for the corresponding candidate season. Scores computed for corresponding candidate seasons are used for identifying which of the candidate seasons represents the seasonal effect of the temporal data.

Abstract: To determine a seasonal effect in temporal data, for each of a plurality of candidate seasons, the following is performed. An interval is defined for the corresponding candidate season. The interval is divided (108) into plural buckets containing respective sets of the temporal data, and statistical measures for corresponding buckets are computed. The computed statistical measures are used to compute (112) a score for the corresponding candidate season. Scores computed for corresponding candidate seasons are used for identifying which of the candidate seasons represents the seasonal effect of the temporal data.