CN106156591A - A kind of smart phone user Transparent Authentication method under cloud environment - Google Patents
A kind of smart phone user Transparent Authentication method under cloud environment Download PDFInfo
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- CN106156591A CN106156591A CN201610544532.0A CN201610544532A CN106156591A CN 106156591 A CN106156591 A CN 106156591A CN 201610544532 A CN201610544532 A CN 201610544532A CN 106156591 A CN106156591 A CN 106156591A
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- mobile phone
- touch screen
- gesture
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
- G06F3/04883—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures for inputting data by handwriting, e.g. gesture or text
Abstract
The invention discloses a kind of smart phone user Transparent Authentication method under cloud environment, including: (1) touch screen gesture data gathers；(2) original gesture data processes；(3) user's gesture feature extracts；(4) add random sample and hide users personal data；(5) model training and parameter output；(6) input touch screen gesture data output result of determination.By the way, smart phone user Transparent Authentication method under cloud environment of the present invention identifies user's touch screen gesture custom owner with authentication intelligent mobile phone pellucidly by the method that have employed incremental learning, ensure that safety and the high efficiency of verification process, user is allowed to enable smart mobile phone to judge the identity of user efficiently and safely by simple contact action, reduce the load of mobile phone terminal and improve the accuracy of judgement, with low cost, wide adaptability, popularizing of smart phone user Transparent Authentication method under cloud environment has market prospect widely.
Description
Technical field
The present invention relates to smart mobile phone field, particularly relate to the smart phone user Transparent Authentication side under a kind of cloud environment
Method.
Background technology
Along with the fast development of smart mobile phone is with universal, smart mobile phone has become as indispensable one in people's life
Basic tool.Smart mobile phone typically include many confidential datas of user, such as address list, bank card information and part privacy
The information such as text, picture, video.In order to prevent mobile phone from losing or problem that is stolen and that cause user privacy information to reveal, one
Effective way is a kind of authenticating identity of mobile phone user method of exploitation, even mobile phone judges whether current user is mobile phone
Owner.If not the owner of mobile phone, then can by forbidding logging in, automatic shutdown, transmission announcement information are gathered around to mobile phone
The various ways such as the person of having reduce privacy leakage risk.Traditional mobile phone user authentication method is to allow user input password code,
And this password code is easily found by opponent and reveals user mobile phone privacy.To this end, Li et al. (L. Li, X. Zhao, G.
Xue. Unobservable Re-authentication for Smartphones, NDSS 2013.) propose a kind of hands
Machine user's Transparent Authentication method.This method by means of present smart mobile phone and all supports the feature of touch screen, by identifying difference
The touch screen gesture feature of user judges the owner of mobile phone, without explicitly inputting user password, so ratio is traditional
Password code method has higher safety.
Owing to this method needs complex model training algorithm, therefore to reduce the expense of mobile phone terminal, generally
All will be placed into the training stage in the unrestricted external server of ability, but do so can cause external server to be used
Family gesture feature and the actual parameter of disaggregated model.Particularly, in order to the parameter making model training is more accurate, the training stage needs
Want substantial amounts of data, thus external server is preferably selected as cloud computing platform.In this case, in order to ensure mobile phone certification
Safety, it is necessary to ensure that cloud platform can not obtain final model parameter.
Summary of the invention
A kind of smart phone user Transparent Authentication method that the technical problem that present invention mainly solves is to provide under cloud environment,
Identify user's touch screen gesture custom owner with authentication intelligent mobile phone pellucidly by the method that have employed incremental learning, protect
Safety and the high efficiency of verification process are demonstrate,proved, it is allowed to user enables smart mobile phone highly effective and safe by simple contact action
Ground judges the identity of user, reduces the load of mobile phone terminal and improves the accuracy of judgement, with low cost, wide adaptability,
Popularizing of smart phone user Transparent Authentication method under cloud environment has market prospect widely.
For solving above-mentioned technical problem, the present invention provides a kind of smart phone user Transparent Authentication method under cloud environment,
Comprise the following steps:
(1) touch screen gesture data gathers:
User, when using smart mobile phone for the first time, carries out random slip several times so that intelligence on smart mobile phone screen
The touch screen gesture data of user collected by mobile phone, comprises the position of contact in slip, time and pressure sequence in gesture data；
(2) original gesture data processes:
After smart mobile phone have collected a number of touch screen gesture data of user, at the gesture data original to these
User's touch screen gesture data is transformed into and meets the data that sorting algorithm process requires by reason, i.e. smart mobile phone, also removes divisor simultaneously
Noise according to and wrong data；
(3) user's gesture feature extracts:
After the original touch screen gesture data of user has been processed by smart mobile phone, gesture data is carried out feature extraction, obtains
User's touch screen gesture feature vector；
(4) add random sample and hide users personal data:
After extracting the characteristic vector of user's all touch screens gesture, this feature vector set is increased some random gestures
Sample is with real, the individual data of hiding user:
A () is first according to the codomain of every category feature Constant numerical values and produces seed characteristics vector randomly as Multi-dimensional Gaussian distribution
Mean vector,
B () then randomly generates the covariance matrix between multidimensional characteristic, carry out stochastical sampling from this distribution and obtain some
Random character vector,
C () finally merges random character vector set and individual subscriber characteristic vector set, upset in set and send out after data order
Give cloud platform and carry out model training；
(5) model training and parameter output:
Cloud computing platform is by defeated for the positive example that the set of eigenvectors cooperation comprising random sample is increment svm classifier algorithm received
Enter, and the set of eigenvectors cooperation of the gesture data of other users collected by platform is counter-example input, carries out disaggregated model
Training, obtains a two classification device, and the training method of described two classification device is to solve following optimization problem:
WhereinRepresent the input of all of positive example and the sample set of counter-example input composition, which includes real user data, the obfuscated data that produced by random fashion in this locality of userWith the disabled user's data in cloud platform, matrix
Diagonal contain the classification logotype of all samples,
According to increment SVM algorithm, the solution of this optimization problem is:
, wherein；
(6) input touch screen gesture data output result of determination:
When smart mobile phone has detected that people, when using it, automatically identifies that whether active user is the owner of this mobile phone, i.e. when
When having user to carry out contact action on mobile phone, gesture data that smart mobile phone record is current also extracts characteristic vector, then counts
Calculate equation below user identity is authenticated:
Wherein, contain the obfuscated data randomly generated before, can be extensive by this decrement operation
Multiple real recognition result；
If result is 1, judge that active user is legal, otherwise judge that active user is illegal, now can carry out next step place
Reason.
In a preferred embodiment of the present invention, described noise and wrong data in step (2) include dividing according to screen
Resolution deletes the off-limits data of coordinate, and gesture path length is too short and cannot be carried out the data of feature extraction.
In a preferred embodiment of the present invention, the characteristic type of the described gesture data in step (3) includes track picture
Element length, track contact number, track persistent period, track transverse axis displacement, track longitudinal axis displacement, average contact spacing, contact
Spacing variance, slip speed, trajectory tortuosity.
In a preferred embodiment of the present invention, described next step in step (6) processes the one including shutdown, reporting to the police
Or it is multiple.
The invention has the beneficial effects as follows: the smart phone user Transparent Authentication method under cloud environment of the present invention is by have employed
The method of incremental learning identifies user's touch screen gesture custom owner with authentication intelligent mobile phone pellucidly, it is ensured that authenticated
The safety of journey and high efficiency, it is allowed to user enables smart mobile phone to judge user efficiently and safely by simple contact action
Identity, reduce the load of mobile phone terminal and improve the accuracy of judgement, with low cost, wide adaptability, under cloud environment
Popularizing of smart phone user Transparent Authentication method has market prospect widely.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, in embodiment being described below required for make
Accompanying drawing be briefly described, it should be apparent that, below describe in accompanying drawing be only some embodiments of the present invention, for
From the point of view of those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain other according to these accompanying drawings
Accompanying drawing, wherein:
Fig. 1 is the structural representation of smart phone user Transparent Authentication method one preferred embodiment under the cloud environment of the present invention.
Detailed description of the invention
Technical scheme in the embodiment of the present invention will be clearly and completely described below, it is clear that described enforcement
Example is only a part of embodiment of the present invention rather than whole embodiments.Based on the embodiment in the present invention, this area is common
All other embodiments that technical staff is obtained under not making creative work premise, broadly fall into the model of present invention protection
Enclose.
Referring to Fig. 1, the embodiment of the present invention includes:
A kind of smart phone user Transparent Authentication method under cloud environment, comprises the following steps:
(1) touch screen gesture data gathers:
User, when using smart mobile phone for the first time, carries out random slip several times so that intelligence on smart mobile phone screen
The touch screen gesture data of user collected by mobile phone, comprises the position of contact in slip, time and pressure sequence in gesture data；
(2) original gesture data processes:
After smart mobile phone have collected a number of touch screen gesture data of user, at the gesture data original to these
User's touch screen gesture data is transformed into and meets the data that sorting algorithm process requires by reason, i.e. smart mobile phone, also removes divisor simultaneously
Noise according to and wrong data；
(3) user's gesture feature extracts:
After the original touch screen gesture data of user has been processed by smart mobile phone, gesture data is carried out feature extraction, obtains
User's touch screen gesture feature vector；
(4) add random sample and hide users personal data:
After extracting the characteristic vector of user's all touch screens gesture, this feature vector set is increased some random gestures
Sample is with real, the individual data of hiding user:
A () is first according to the codomain of every category feature Constant numerical values and produces seed characteristics vector randomly as Multi-dimensional Gaussian distribution
Mean vector,
B () then randomly generates the covariance matrix between multidimensional characteristic, carry out stochastical sampling from this distribution and obtain some
Random character vector,
C () finally merges random character vector set and individual subscriber characteristic vector set, upset in set and send out after data order
Give cloud platform and carry out model training；
(5) model training and parameter output:
Cloud computing platform is by defeated for the positive example that the set of eigenvectors cooperation comprising random sample is increment svm classifier algorithm received
Enter, and the set of eigenvectors cooperation of the gesture data of other users collected by platform is counter-example input, carries out disaggregated model
Training, obtains a two classification device, and the training method of described two classification device is to solve following optimization problem:
WhereinRepresent the input of all of positive example and the sample set of counter-example input composition, which includes real user data, the obfuscated data that produced by random fashion in this locality of userWith the disabled user's data in cloud platform, matrix
Diagonal contain the classification logotype of all samples,
According to increment SVM algorithm, the solution of this optimization problem is:
, wherein；
(6) input touch screen gesture data output result of determination:
When smart mobile phone has detected that people, when using it, automatically identifies that whether active user is the owner of this mobile phone, i.e. when
When having user to carry out contact action on mobile phone, gesture data that smart mobile phone record is current also extracts characteristic vector, then counts
Calculate equation below user identity is authenticated:
Wherein, contain the obfuscated data randomly generated before, can be extensive by this decrement operation
Multiple real recognition result；
If result is 1, judge that active user is legal, otherwise judge that active user is illegal, now can carry out next step place
Reason.
Preferably, the described noise in step (2) and wrong data include deleting coordinate beyond model according to screen resolution
The data enclosed, and gesture path length is too short and cannot be carried out the data of feature extraction.
Preferably, the characteristic type of the described gesture data in step (3) includes track length in pixels, track contact
Number, track persistent period, track transverse axis displacement, track longitudinal axis displacement, average contact spacing, contact spacing variance, slip
Speed, trajectory tortuosity.
Preferably, in step (6) described next step process include shutdown, report to the police one or more.
Smart phone user Transparent Authentication method under cloud environment of the present invention provides the benefit that:
Identify that user's touch screen gesture is accustomed to having with authentication intelligent mobile phone pellucidly by the method that have employed incremental learning
Person, it is ensured that the safety of verification process and high efficiency, it is allowed to user enables smart mobile phone high by simple contact action
Effect judges the identity of user safely, reduces the load of mobile phone terminal and improves the accuracy of judgement, with low cost, adaptability
Extensively.
The foregoing is only embodiments of the invention, not thereby limit the scope of the claims of the present invention, every utilize this
Equivalent structure or equivalence flow process that bright description is made convert, or are directly or indirectly used in other relevant technology neck
Territory, is the most in like manner included in the scope of patent protection of the present invention.
Claims (4)
1. the smart phone user Transparent Authentication method under a cloud environment, it is characterised in that comprise the following steps:
(1) touch screen gesture data gathers:
User, when using smart mobile phone for the first time, carries out random slip several times so that intelligence on smart mobile phone screen
The touch screen gesture data of user collected by mobile phone, comprises the position of contact in slip, time and pressure sequence in gesture data；
(2) original gesture data processes:
After smart mobile phone have collected a number of touch screen gesture data of user, at the gesture data original to these
User's touch screen gesture data is transformed into and meets the data that sorting algorithm process requires by reason, i.e. smart mobile phone, also removes divisor simultaneously
Noise according to and wrong data；
(3) user's gesture feature extracts:
After the original touch screen gesture data of user has been processed by smart mobile phone, gesture data is carried out feature extraction, obtains
User's touch screen gesture feature vector；
(4) add random sample and hide users personal data:
After extracting the characteristic vector of user's all touch screens gesture, this feature vector set is increased some random gestures
Sample is with real, the individual data of hiding user:
A () is first according to the codomain of every category feature Constant numerical values and produces seed characteristics vector randomly as Multi-dimensional Gaussian distribution
Mean vector,
B () then randomly generates the covariance matrix between multidimensional characteristic, carry out stochastical sampling from this distribution and obtain some
Random character vector,
C () finally merges random character vector set and individual subscriber characteristic vector set, upset in set and send out after data order
Give cloud platform and carry out model training；
(5) model training and parameter output:
Cloud computing platform is by defeated for the positive example that the set of eigenvectors cooperation comprising random sample is increment svm classifier algorithm received
Enter, and the set of eigenvectors cooperation of the gesture data of other users collected by platform is counter-example input, carries out disaggregated model
Training, obtains a two classification device, and the training method of described two classification device is to solve following optimization problem:
WhereinRepresent the input of all of positive example and the sample set of counter-example input composition, which includes real user data, the obfuscated data that produced by random fashion in this locality of userWith the disabled user's data in cloud platform, matrix
Diagonal contain the classification logotype of all samples,
According to increment SVM algorithm, the solution of this optimization problem is:
, wherein；
(6) input touch screen gesture data output result of determination:
When smart mobile phone has detected that people, when using it, automatically identifies that whether active user is the owner of this mobile phone, i.e. when
When having user to carry out contact action on mobile phone, gesture data that smart mobile phone record is current also extracts characteristic vector, then counts
Calculate equation below user identity is authenticated:
Wherein, contain the obfuscated data randomly generated before, can be recovered by this decrement operation
Real recognition result；
If result is 1, judge that active user is legal, otherwise judge that active user is illegal, now can carry out next step place
Reason.
Smart phone user Transparent Authentication method under cloud environment the most according to claim 1, it is characterised in that step
(2) described noise and wrong data in include deleting the off-limits data of coordinate, and gesture rail according to screen resolution
Mark length is too short and cannot be carried out the data of feature extraction.
Smart phone user Transparent Authentication method under cloud environment the most according to claim 1, it is characterised in that step
(3) characteristic type of the described gesture data in includes track length in pixels, track contact number, track persistent period, track
Transverse axis displacement, track longitudinal axis displacement, average contact spacing, contact spacing variance, slip speed, trajectory tortuosity.
Smart phone user Transparent Authentication method under cloud environment the most according to claim 1, it is characterised in that step
(6) in described next step process include shutdown, report to the police one or more.
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