Disclosure of Invention
In view of the above, the application provides a data processing method, a device, equipment and a storage medium based on privacy protection, which can effectively identify and classify privacy information, and simultaneously, automatically execute data access rules through intelligent contracts, thereby improving the efficiency and the safety of data processing.
The application provides a data processing method based on privacy protection, which comprises the following steps:
acquiring the preprocessed feature data, calling a trained deep learning model to identify the feature data, and determining privacy information and type information corresponding to the privacy information;
Determining an information processing method according to the type information, and encrypting the privacy information based on the privacy information processing method to obtain encrypted information;
Responding to a user viewing instruction, and acquiring a data access right corresponding to the user viewing instruction through an intelligent contract;
And decrypting the encrypted information according to the data access authority to obtain private data.
Optionally, the specific steps of acquiring the preprocessed feature data include:
initial data of a user is obtained, natural language is obtained by adopting a natural language processing method, and the natural language is converted into machine language through part-of-speech quantization.
Optionally, the invoking the trained deep learning model identifies the feature data, the deep learning model including:
The deep learning model adopts a cyclic neural network model, wherein the cyclic neural network model comprises a traditional cyclic neural network, a long-term and short-term memory network or a gating cyclic unit.
Optionally, the determining an information processing method according to the type information, and encrypting the sensitive information based on the sensitive information processing method to obtain encrypted information includes:
determining a target encryption method in a preset encryption mapping table according to the type information;
and encrypting the privacy information based on the target encryption method to obtain encrypted information.
Optionally, the responding to the user viewing instruction obtains the data access right corresponding to the user viewing instruction through an intelligent contract, and the method further comprises:
Constructing a blockchain framework for data storage and access, wherein the blockchain framework receives an access request about target data in a user viewing instruction of a user demand party;
Executing the intelligent contract of the blockchain framework in a trusted execution environment to judge whether the user checking instruction meets the access right condition of the target data;
if the data access permission is met, the corresponding data access permission of the user demand party is configured.
Optionally, the acquiring the preprocessed feature data, calling a trained deep learning model to identify the feature data, and determining privacy information and type information corresponding to the privacy information, where the method further includes:
and the privacy information selects a logic expression corresponding to the hierarchical mode logic as a representation according to the type information.
Optionally, the method for determining an information processing method according to the type information, and encrypting the private information based on the private information processing method to obtain encrypted information, where the method further includes:
acquiring the privacy information, and performing feature extraction by adopting a 3D sparse convolution network to obtain 3D sparse features;
determining a target encryption method in a preset encryption mapping table based on the 3D sparse features;
and combining the 3D sparse features to generate an encryption key, and encrypting the privacy information to obtain encryption information.
Correspondingly, the application provides a data processing device based on privacy protection, which comprises:
the data acquisition module is used for acquiring the preprocessed characteristic data, calling a trained deep learning model to identify the characteristic data, and determining privacy information and type information corresponding to the privacy information;
the data encryption module is used for determining an information processing method according to the type information, encrypting the privacy information based on the privacy information processing method and obtaining encrypted information;
the access right module is used for responding to a user viewing instruction and acquiring the data access right corresponding to the user viewing instruction through an intelligent contract;
and the data decryption module is used for decrypting the encrypted information according to the data access authority to obtain private data.
The application further provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the privacy protection-based data processing method according to any one of the above are implemented when the processor executes the program.
On the basis of this, the application also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the privacy-preserving-based data processing method as defined in any one of the above.
The application provides a data processing method, device, equipment and storage medium based on privacy protection, which are used for acquiring preprocessed feature data, calling a trained deep learning model to identify the feature data, determining privacy information and type information corresponding to the privacy information, determining an information processing method according to the type information, encrypting the privacy information based on the privacy information processing method to obtain encrypted information, responding to a user checking instruction, acquiring data access rights corresponding to the user checking instruction through an intelligent contract, and decrypting the encrypted information according to the data access rights to obtain the privacy data. The method can effectively identify and classify the privacy information through the trained deep learning model, and simultaneously, the data access rule is automatically executed through the intelligent contract, so that the efficiency and the safety of data processing are improved.
Detailed Description
The following description of the embodiments of the present application will be made in detail and with reference to the accompanying drawings, wherein it is apparent that the embodiments described are only some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application. The various embodiments described below and their technical features can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic view of a data processing method based on privacy protection according to an embodiment of the present application.
In the application scenario of fig. 1, the computing device 101 may acquire the preprocessed feature data, invoke the trained deep learning model to identify the feature data, determine the privacy information and the type information corresponding to the privacy information, determine an information processing method according to the type information, encrypt the privacy information based on the privacy information processing method to obtain encrypted information, the computing device 101 may respond to a user viewing instruction to acquire the data access right corresponding to the user viewing instruction through an intelligent contract, and decrypt the encrypted information according to the data access right by the computing device 101 to obtain the privacy data.
It should be noted that, the computing device 101 may be hardware, or may be software. When the computing device 101 is hardware, it may be implemented as a distributed cluster of multiple servers or terminal devices, or as a single server or single terminal device. When the computing device 101 is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
In addition, the application can be applied to the identification and protection of the privacy information of the medical health data, the protection of the client information in the financial service or the safety management of the files in enterprises. For example, in the field of medical health, the technical scheme is used for carrying out sensitive information identification and encryption processing on medical records of patients, identifying sensitive data (such as medical history, drug response and the like) in personal health information through a deep learning model, and adopting a blockchain technology to ensure the safety and compliance of data access.
Referring to fig. 2, fig. 2 shows a flowchart of a data processing method based on privacy protection according to an embodiment of the present specification.
S1, acquiring the preprocessed feature data, calling a trained deep learning model to identify the feature data, and determining privacy information and type information corresponding to the privacy information.
In one possible implementation, initial data of a user is obtained, natural language is obtained by adopting a natural language processing method, the natural language is converted into machine language through part-of-speech quantization, and the natural language processing method comprises data preprocessing, feature extraction, key information identification, context understanding and association analysis.
Specifically, the natural language processing method comprises the following specific steps:
S101, data preprocessing, namely cleaning and formatting initial data of a user, including removing irrelevant characters (such as punctuation marks and special symbols), unifying text formats, vocabulary segmentation and the like, wherein the cleaning and formatting are used for reducing complexity of model processing and improving accuracy and efficiency of subsequent analysis.
S102, converting the text of the initial data into a numerical form, namely a feature vector by adopting methods such as TF-IDF, word2Vec and the like, so that semantic information of the text can be conveniently converted into a form which can be identified by a model, and the subsequent identification of sensitive information is facilitated.
S103, an NLP model (a pre-training model of BERT and GPT) is adopted to analyze the feature vectors, and key information in the feature vectors, such as personal name, address, telephone number and the like, is identified, so that privacy information can be conveniently and rapidly extracted from a large amount of data, and a basis is provided for subsequent information classification and processing.
S104, analyzing context information of the text on the basis of the key information, understanding the relation and meaning among the information, and ensuring that the identification of the privacy information is not only dependent on the matching of the key words, but also combined with the context environment, thereby improving the accuracy and reliability of the identification.
In one possible implementation, invoking the trained deep learning model to identify the feature data and determining the privacy information and the type information corresponding to the privacy information includes invoking the trained deep learning model to determine whether the feature data contains the privacy information, and invoking the trained deep learning model to determine the type information of the privacy information when the feature data contains the target privacy information.
Specifically, during deep learning model training and optimization, key steps comprise preprocessing and feature engineering, model training, evaluation and verification, super-parameter adjustment and model optimization. The steps need to comprehensively consider aspects of data characteristics, model architecture, super-parameter adjustment, performance evaluation, continuous learning and the like so as to ensure that the model can show good performance and accuracy in training and practical application. The specific steps of training and optimizing the deep learning model comprise:
Preprocessing and feature engineering, namely preprocessing data, such as text cleaning, standardization and word embedding, and carrying out necessary feature engineering to extract the beneficial features of model training, and the data quality is improved through preprocessing and feature engineering, so that the model can learn and extract useful information more effectively.
Model training, namely training a deep learning model by using the prepared data set, and continuously adjusting model parameters (such as learning rate, hidden layer unit number, batch processing size and the like) in the training process to optimize the training process, wherein the model learns to identify and classify privacy information from the data set through the training process.
And (3) evaluating and verifying the performance of the model by adopting a cross verification method, monitoring key indexes such as accuracy, recall rate and F1 score, ensuring that the model has good generalization capability, and keeping stable performance on different data sets.
And (3) super-parameter adjustment and model optimization, wherein the super-parameter of the model is adjusted based on the performance evaluation result, and the adjustment possibly involved comprises the steps of changing a network architecture, optimizing a regularization strategy, adjusting a learning rate and the like, so that the accuracy and the efficiency of the model are further improved through continuous adjustment and optimization.
Model deployment and continuous learning, namely retraining and updating the model by periodically using new data so as to adapt to new data modes and changes, keep timeliness and accuracy of the model and adapt to continuously changing data environments and requirements.
In one possible implementation, the feature data is identified by invoking a trained deep learning model, where the deep learning model includes:
the deep learning model adopts a circulating neural network model, the circulating neural network model comprises a traditional circulating neural network, a long-term and short-term memory network or a gating circulating unit, the selecting of the circulating neural network model needs to be selected according to the characteristics and the requirements of data, and different circulating neural network architectures have different characteristics and application scenes.
For example, for processing long sequence data, such as long text or time sequence data, long and short term memory networks and gating loops are the preferred choice, enabling efficient resolution of the gradient vanishing problem, allowing the network to learn long term dependencies over longer sequences. The long-period memory network controls the flow of information by introducing a gate structure, so that the problem of gradient disappearance is avoided, the gate control mechanism allows information to be transmitted between time steps, and unimportant information can be forgotten, so that the long-period memory network is particularly suitable for processing data with long-term dependency. The gating circulation unit is another common circulation neural network variant, the internal structure of the gating circulation unit is similar to that of a long-period memory network, but the gating circulation unit is relatively simple in structure, the efficiency is improved by reducing the number of parameters and simplifying the calculation process, and meanwhile, the good performance of the long-period memory network is maintained. In addition, for tasks such as short sequence data or text classification, the simple structure of the traditional recurrent neural network makes it easier to train and works well when processing short sequences.
In one possible implementation, the type information is used to define different levels of privacy, and may be classified into high-level privacy, medium-level privacy and general privacy, so as to define the classification standard of the privacy information, ensure the accuracy and consistency of subsequent processing, and determine the corresponding marking rule according to the level of privacy. In particular, information related to personal privacy is marked as highly private, and information related to company internal data is moderately private.
In another possible implementation manner, on the basis of automatic marking and classification of the deep learning model, manual auditing can be performed to ensure the accuracy and rationality of marking, and the manual auditing content can involve checking marking results, correcting wrong marks and adjusting classification rules, so that misjudgment of the model is prevented, and the final accuracy and reliability of information classification are ensured.
S2, determining an information processing method according to the type information, and encrypting the privacy information based on the privacy information processing method to obtain encrypted information.
In one possible implementation, the method for processing the information according to the type information and encrypting the private information based on the method for processing the private information to obtain the encrypted information comprises the steps of determining a target encryption method according to the type information in a preset encryption mapping table, and encrypting the private information based on the target encryption method to obtain the encrypted information.
For example, different encryption methods may be employed for different types of private information. For example, private information such as an individual or a company may be encrypted using an algorithm such as AES, and a specific mode using the AES encryption algorithm including AES-128, AES-192 or AES-256 may be determined according to the type of the private information, the main difference of the modes being that the longer the key is, the higher the encryption strength is, and highly private information may be encrypted using AES-256. The encrypted sensitive information is stored in a database or blockchain framework, for example, using a database management system such as MySQL, oracle, etc. When storing, it is noted that the encrypted information needs to be associated with a corresponding user ID for subsequent data querying and processing.
And S3, responding to the user checking instruction, and acquiring the data access right corresponding to the user checking instruction through the intelligent contract.
In a possible implementation manner, in the process of responding to a user checking instruction, acquiring user identity information corresponding to the user checking instruction and determining data access permission based on the user identity information, the method further comprises the steps of constructing a blockchain framework for data storage and access, receiving an access request about target data in the user checking instruction of a user demand party by the blockchain framework, executing an intelligent contract of the blockchain framework in a trusted execution environment to judge whether the user checking instruction meets the access permission condition of the target data or not, and if so, configuring the corresponding data access permission of the user demand party.
Specifically, the blockchain framework provides a non-tamperable and highly transparent environment for data access and audit, is beneficial to improving the safety, the authenticity and the accuracy of the data, reducing the audit cost, improving the audit efficiency, enhancing the internal control and the external supervision, selecting a proper blockchain type, such as a public chain, a private chain or a alliance chain, in the building process when the blockchain framework suitable for data storage and access is built, and determining the optimal blockchain type according to the data safety and sharing requirements.
Specifically, intelligent contracts are deployed on the blockchain framework and are used for automatically managing access rights of data, and data access rules are automatically executed through the intelligent contracts, so that the efficiency and the safety of data processing are improved. After deployment of the smart contract, secure access control mechanisms are implemented, including authentication and authorization, using techniques such as digital signature and key management to ensure that only authorized users can access the data, and all data access actions are recorded on the blockchain, including access time, visitor identity, and accessed data content. In addition, the blockchain framework is maintained and updated regularly, including upgrading intelligent contracts, optimizing the performance of the blockchain framework and the like, so that the blockchain framework is ensured to adapt to the continuously changing technology and business requirements, and the safety and the high efficiency of the blockchain framework are maintained.
And S4, decrypting the encrypted information according to the data access authority to obtain the private data.
In practical application, for a user with authority to access the private information, the corresponding private information needs to be decrypted to generate the original private data. The decryption may be performed according to a specific desensitization algorithm and encryption scheme, such as AES encryption algorithm, symmetric encryption algorithm, asymmetric encryption algorithm, etc.
In one possible implementation manner, the method further includes acquiring the preprocessed feature data, calling a trained deep learning model to identify the feature data, and determining the privacy information and the type information corresponding to the privacy information:
the privacy information selects a logic expression corresponding to the hierarchical mode logic as a representation according to the type information, and the privacy information can be described and processed more carefully through the multi-level structure of the hierarchical mode logic, so that the identification accuracy is improved.
In particular, hierarchical modal logic (Graded Modal Logic) is an extended modal logic that introduces the concept of "level" or "number" on the basis of conventional modal logic. In conventional modal logic, modal operators generally represent the necessity (≡) and the likelihood (-), whereas in hierarchical modal logic, these modal operators are given quantitative significance, the concept of "at least how many" or "not more than how many" can be expressed, while the logical expression in hierarchical modal logic is associated with a specific level or quantity. For example, a hierarchical necessity operator may indicate that "at least n instances satisfy a certain condition". In this embodiment, the identified privacy information (such as personal data, company information, etc.) is converted into a logic expression in the hierarchical modal logic, and the characteristics and level of the privacy information are precisely described by using the expression capability of the hierarchical modal logic. For example, different logical expressions are defined to represent privacy levels of information. For example, +.1 can represent general privacy, +.2 represents moderate privacy, +.3 represents high privacy.
In one possible implementation manner, the method for processing information is determined according to the type information, and the privacy information is encrypted based on the method for processing privacy information to obtain encrypted information, and the method further includes:
the method comprises the steps of obtaining privacy information, carrying out feature extraction by adopting a 3D sparse convolution network to obtain 3D sparse features, determining a target encryption method in a preset encryption mapping table based on the 3D sparse features, generating an encryption key by combining the 3D sparse features, and encrypting the privacy information to obtain encryption information.
Through the 3D representation of the privacy information, the 3D sparse convolution can be applied to perform feature extraction, a corresponding encryption algorithm can be designed based on the extracted features, and the data can be effectively encrypted by combining the features extracted by the 3D sparse convolution. By the customized encryption algorithm design, the security and the uniqueness of the encryption process can be improved. Specific application scenarios include those requiring custom protection of a large variety of data, such as financial institutions, healthcare institutions, and government authorities. By combining the 3D sparse convolution extracted characteristics to design an encryption algorithm, the security of sensitive data in the storage and transmission processes can be ensured, and meanwhile, the encryption strategy is dynamically adjusted according to the characteristics and the risk level of the data, so that the efficient and safe data protection effect is achieved.
Specifically, the 3D representation of the private information is to convert the identified sensitive information (such as text, image, etc.) into a three-dimensional data format, in which the information is converted into a three-dimensional matrix, each dimension represents a different feature of the information, and by converting the three-dimensional data format, the 3D sparse convolution network can be used to process the data more effectively, thereby improving the efficiency and effect of the subsequent encryption step.
The application provides a data processing method, device, equipment and storage medium based on privacy protection, which are used for acquiring preprocessed feature data, calling a trained deep learning model to identify the feature data, determining privacy information and type information corresponding to the privacy information, determining an information processing method according to the type information, encrypting the privacy information based on the privacy information processing method to obtain encrypted information, responding to a user checking instruction, acquiring data access rights corresponding to the user checking instruction through an intelligent contract, and decrypting the encrypted information according to the data access rights to obtain the privacy data. The method can effectively identify and classify the privacy information through the trained deep learning model, and simultaneously, the data access rule is automatically executed through the intelligent contract, so that the efficiency and the safety of data processing are improved.
As shown in fig. 2, the present application further provides a data processing apparatus based on privacy protection, including:
The data acquisition module 101 is configured to acquire the preprocessed feature data, invoke a trained deep learning model to identify the feature data, and determine privacy information and type information corresponding to the privacy information;
A data encryption module 102, configured to determine an information processing method according to type information, and encrypt the private information based on the private information processing method to obtain encrypted information;
The access right module 103 is used for responding to the user viewing instruction and acquiring the data access right corresponding to the user viewing instruction through the intelligent contract;
The data decryption module 104 is configured to decrypt the encrypted information according to the data access rights to obtain the private data.
In addition, an embodiment of the present application further provides an electronic device, as shown in fig. 3, which shows a schematic structural diagram of the electronic device according to the embodiment of the present application, specifically:
The electronic device may include one or more processing cores 'processors 301, one or more computer-readable storage media's memory 302, power supply 303, and input unit 304, among other components. Those skilled in the art will appreciate that the electronic device structure shown in fig. 3 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components. Wherein:
The processor 301 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 302, and calling data stored in the memory 302, thereby performing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores, and preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor primarily processes operating systems, user interfaces, application programs, etc., and the modem processor primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301.
The memory 302 may be used to store software programs and modules, and the processor 301 executes various functional applications and image information communication methods by executing the software programs and modules stored in the memory 302. The memory 302 may mainly include a storage program area that may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), etc., and a storage data area that may store data created according to the use of the electronic device, etc. In addition, memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
The electronic device further comprises a power supply 303 for powering the various components, preferably the power supply 303 is logically connected to the processor 301 by a power management system, whereby the functions of managing charging, discharging, and power consumption are performed by the power management system. The power supply 303 may also include one or more of any components, such as a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The electronic device may further comprise an input unit 304, which input unit 304 may be used for receiving input digital or character information and for generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.
Although not shown, the electronic device may further include a display unit or the like, which is not described herein. In particular, in this embodiment, the processor 301 in the electronic device loads executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 executes the application programs stored in the memory 302, so as to implement various functions as follows:
The method comprises the steps of obtaining preprocessed characteristic data, calling a trained deep learning model to identify the characteristic data, determining privacy information and type information corresponding to the privacy information, determining an information processing method according to the type information, encrypting the privacy information based on the privacy information processing method to obtain encrypted information, responding to a user checking instruction, obtaining data access permission corresponding to the user checking instruction through an intelligent contract, and decrypting the encrypted information according to the data access permission to obtain the privacy data.
The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.
The application provides electronic equipment, which is characterized in that preprocessed characteristic data are obtained, a trained deep learning model is called to identify the characteristic data, privacy information and type information corresponding to the privacy information are determined, an information processing method is determined according to the type information, the privacy information is encrypted based on the privacy information processing method to obtain encrypted information, a user checking instruction is responded, data access rights corresponding to the user checking instruction are obtained through an intelligent contract, and the encrypted information is decrypted according to the data access rights to obtain the privacy data. The method can effectively identify and classify the privacy information through the trained deep learning model, and simultaneously, the data access rule is automatically executed through the intelligent contract, so that the efficiency and the safety of data processing are improved.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present application provides a storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the privacy-based data processing methods provided by the embodiments of the present application. For example, the instructions may perform the steps of:
The method comprises the steps of obtaining preprocessed characteristic data, calling a trained deep learning model to identify the characteristic data, determining privacy information and type information corresponding to the privacy information, determining an information processing method according to the type information, encrypting the privacy information based on the privacy information processing method to obtain encrypted information, responding to a user checking instruction, obtaining data access permission corresponding to the user checking instruction through an intelligent contract, and decrypting the encrypted information according to the data access permission to obtain the privacy data.
The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.
The storage medium may include a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or the like.
The instructions stored in the storage medium can execute the steps in any privacy protection-based data processing method provided by the embodiment of the present application, so that the beneficial effects that any privacy protection-based data processing method provided by the embodiment of the present application can be realized, and detailed descriptions of the foregoing embodiments are omitted herein.
Although the application has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The present application includes all such modifications and alterations and is limited only by the scope of the following claims.
That is, the foregoing embodiments of the present application are merely examples, and are not intended to limit the scope of the present application, and all equivalent structures or equivalent processes using the descriptions of the present application and the accompanying drawings, such as the combination of technical features of the embodiments, or direct or indirect application in other related technical fields, are included in the scope of the present application.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
In addition, the present application may be identified by the same or different reference numerals for structural elements having the same or similar characteristics. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present application, the above description is given to enable any person skilled in the art to make and use the application. In the above description, various details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been shown in detail to avoid unnecessarily obscuring the description of the application. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.