CN110365679B - Context-aware cloud data privacy protection method based on crowdsourcing evaluation - Google Patents
Context-aware cloud data privacy protection method based on crowdsourcing evaluation Download PDFInfo
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Abstract
The invention aims to provide a context-aware cloud data privacy protection method based on crowdsourcing evaluation, which can provide data privacy protection service for a user in a cloud data sharing context. The method can sense the situation of user data sharing, including data type, content type, data owner and the like, and provides different data sharing strategies according to different situations, thereby effectively protecting the privacy of user data; meanwhile, collective intelligence of cloud users is integrated in a crowdsourcing mode, and based on a project reaction theory, data sensitivity is comprehensively and effectively evaluated in an objective evaluation and interactive crowdsourcing mode from the perspective of a system, accuracy of data privacy safety risk evaluation is further improved, and data privacy protection service under a cloud data sharing situation is provided for users.
Description
Technical Field
The invention belongs to the field of data privacy security, and particularly relates to a context-aware cloud data privacy protection method based on crowdsourcing evaluation.
Background
Under the era of mobile internet and cloud computing, users can enjoy various cloud application services including taxi taking, takeaway, mobile payment and the like by providing identity information for registration. However, users are exposed to the risk of private data leakage while enjoying various convenient application services. For example, the insecurity of netizens is increased in cases of secret divulgence such as data theft of 32 universal users in china 32 of the times of the cable television company of the united states, password leakage of a Tumblr account number of 6500 universal mailbox of a light blog website, public sale of 1.67 million accounts in black city of LinkedIn and the like. Ordinary users cannot protect the data security of themselves, and even do not realize the identity data of the users and the behavior data of service enjoying, the privacy information of travel laws, eating habits, consumption habits, interests and the like of the users is disclosed.
For the problem of privacy disclosure, the existing encryption-based method is difficult to be applied to a cloud scene, for example, if user data is encrypted ciphertext in the cloud, the file cannot be shared with other users. Other privacy preserving methods focus on privacy preserving signatures in the digital signature process. The method focuses on privacy protection of the consumers in different consumption scenes, and is not considered in different cloud data sharing scenes.
Disclosure of Invention
The invention provides a context-aware cloud data privacy protection method based on crowdsourcing evaluation, which solves the problem that an ordinary user cannot judge privacy disclosure risks independently, and particularly cannot estimate the disclosure risks after data is shared when the data is shared; according to the invention, the problem of data leakage is solved according to the lowest data privacy leakage storage mode in different cloud data sharing scenes.
The technical scheme adopted by the invention is as follows:
a context-aware cloud data privacy protection method based on crowd-sourcing evaluation comprises the following steps:
s1, the client generates a context dictionary according to the context information of the data file, and then the client sends the generated context dictionary to the server as a sensitivity query list;
s2, inquiring and establishing a data sensitivity value: the server inquires each situation in the situation List in the data sensitivity value database after receiving the client inquiry List, and directly returns a data sensitivity value (List) to the client if the situation is evaluated; otherwise, if the evaluation is not performed, the server side returns a List; simultaneously, designing the uploaded situation information into a crowdsourcing task, inviting the user to evaluate the sensitivity of the data together to obtain a data sensitivity value, and then storing the data sensitivity value obtained after crowdsourcing evaluation into a data sensitivity value database for the server side to inquire;
s3, evaluation of privacy risk exposure value: the server calculates objective probability value of the data privacy leakage event according to the context information received in the step S2 and the leakage probability corresponding to the same previous context information in the server; performing comprehensive evaluation according to the objective probability value and the data sensitivity value obtained by crowd sourcing evaluation in the second step to obtain a privacy risk leakage value;
s4, strategy recommendation: forming and recommending a plurality of sharing strategies with different risks according to the privacy risk leakage value evaluated in the step S3;
s5, applying a strategy: and according to the different risk sharing strategies obtained in the step S4, selecting a sharing strategy corresponding to the risk to apply to the data, and sharing the data processed by the corresponding sharing strategy to the cloud.
At present, users generally lack basic privacy protection consciousness, privacy safety of the users is often ignored, and a word bank under the privacy protection situation is created to help the users to evaluate the privacy protection consciousness degree of different users and to react to privacy protection in different aspects; based on a context thesaurus, a risk assessment algorithm is designed to determine sensitivity in a data sharing process and security policy (policies) setting of a user on privacy; the method can sense the situation of user data sharing, including data type, content type, data owner and the like, and provides different data sharing strategies according to different situations, thereby effectively protecting the privacy of user data; meanwhile, collective intelligence of cloud users is integrated in a crowdsourcing mode, and based on a project reaction theory, data sensitivity is comprehensively and effectively evaluated in an objective evaluation and interactive crowdsourcing mode from the perspective of a system, accuracy of data privacy safety risk evaluation is further improved, and data privacy protection service under a cloud data sharing situation is provided for users.
Further, in step S1, the context dictionary includes identity information of the sharee and/or a sharing policy and/or a sharing file type; the context dictionary defines the context of the shared data.
Further, before the context dictionary is sent in step S1, the information of the context dictionary is processed, and the processing mode includes processing in an anonymous form and/or an encrypted form and/or a data obfuscation form.
Further, after the data sensitivity value obtained in step S2 is returned to the client, the client caches information of the context dictionary corresponding to the data sensitivity value, and provides the client with a query; after the client generates the context dictionary, inquiring corresponding data sensitivity values for the information of the context dictionary in a cache database of the client so that the client can directly obtain the data sensitivity values; if the client side inquires the corresponding data sensitivity value, the information of the situation dictionary is not sent to the server any more; otherwise, if the client does not inquire the corresponding data sensitivity value in the cache database, the step S1 is performed.
Further, after the data is uploaded to the server according to the selected sharing policy in step S5, the uploaded data is shared.
Further, in step S3, a comprehensive evaluation is performed according to the data sensitivity value obtained in step S and the probability value of the occurrence of the data privacy leakage event, where the comprehensive evaluation employs a weighted evaluation function and/or a product evaluation function.
Further, the sharing policies of different risks in step S4 at least include two policies of completely disclosing shared data and not disclosing shared data at all; the full disclosure is to directly disclose and share data; the complete disclosure is to encrypt the data and partially hide the metadata of the data.
Further, the partial hiding of the metadata of the data includes data owner information and/or time information of last modification.
Further, after the data upload is completed each time in step S5, the context information and the corresponding sharing policy of the data upload sharing are sent to the cloud, and the evaluation history of this time is recorded for the next evaluation and crowd sourcing evaluation analysis.
The invention has the following advantages and beneficial effects:
1. the invention can provide data privacy protection service for the user in the cloud data sharing situation. At present, users generally lack basic privacy protection consciousness, privacy safety of the users is often ignored, and a word bank under the privacy protection situation is created to help the users to evaluate the privacy protection consciousness degree of different users and to react to privacy protection in different aspects; based on a context thesaurus, a risk assessment algorithm is designed to determine sensitivity in a data sharing process and security policy (policies) setting of a user on privacy; the method can sense the situation of user data sharing, including data type, content type, data owner and the like, and provides different data sharing strategies according to different situations, thereby effectively protecting the privacy of user data; meanwhile, collective intelligence of cloud users is integrated in a crowdsourcing mode, and based on a project reaction theory, data sensitivity is comprehensively and effectively evaluated in an objective evaluation and interactive crowdsourcing mode from the perspective of a system, accuracy of data privacy safety risk evaluation is further improved, and data privacy protection service under a cloud data sharing situation is provided for users.
2. The context awareness is embodied in that different contexts in the data sharing process can be accurately described by establishing a context dictionary, the data privacy leakage risk is evaluated according to the different contexts, and corresponding sharing strategies are recommended; the method has the advantages that a crowdsourcing evaluation mode is adopted, the sensitivity of the user to different data privacy is scientifically evaluated by combining a project reaction theory, the data sensitivity values of the user under different scenes are obtained by using collective wisdom and shared wisdom, and more personalized data privacy protection strategies are provided for different purposes.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a diagram of a context dictionary for data sharing according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
It should be understood that the terms first, second, etc. are used merely for distinguishing between descriptions and are not intended to indicate or imply relative importance. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time, and the term "/and" is used herein to describe another association object relationship, which means that two relationships may exist, for example, A/and B, may mean: a alone, and both a and B alone, and further, the character "/" in this document generally means that the former and latter associated objects are in an "or" relationship.
It is to be understood that in the description of the present invention, the terms "upper", "vertical", "inside", "outside", and the like, refer to an orientation or positional relationship that is conventionally used for placing the product of the present invention, or that is conventionally understood by those skilled in the art, and are used merely for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore should not be considered as limiting the present invention.
It will be understood that when an element is referred to as being "connected," "connected," or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly adjacent" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between … …" versus "directly between … …", "adjacent" versus "directly adjacent", etc.).
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," and "connected" are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
Example 1:
as shown in fig. 1 and fig. 2, the embodiment provides a context-aware cloud data privacy protection method based on crowdsourcing evaluation, which includes the following steps:
s1, the client generates a context dictionary according to the context information of the data file, and then the client sends the generated context dictionary to the server as a sensitivity query list;
s2, inquiring and establishing a data sensitivity value: after receiving the client query list, the server queries each situation in the situation list in the data sensitivity value database, and if the situation is evaluated, the server directly returns a data sensitivity value to the client; otherwise, if the situation information is not evaluated, the server end designs the uploaded situation information into a crowdsourcing task, invites the user to evaluate the sensitivity of the data together to obtain a data sensitivity value, and then stores the data sensitivity value obtained after crowdsourcing evaluation into the data sensitivity value database for the server end to inquire;
s3, evaluation of privacy risk exposure value: the server calculates objective probability value of the data privacy leakage event according to the context information received in the step S2 and the leakage probability corresponding to the same previous context information in the server; performing comprehensive evaluation according to the objective probability value and the data sensitivity value obtained by crowd sourcing evaluation in the second step to obtain a privacy risk leakage value;
s4, strategy recommendation: forming and recommending a plurality of sharing strategies with different risks according to the privacy risk leakage value evaluated in the step S3;
s5, applying a strategy: and according to the different risk sharing strategies obtained in the step S4, selecting a sharing strategy corresponding to the risk to apply to the data, and sharing the data processed by the corresponding sharing strategy to the cloud.
And judging the setting of each sharing operation, the sharing situation and the sharing strategy by adopting a crowdsourcing mode based on the crowdsourcing data sensitivity evaluation. The wisdom of many users is utilized to determine the risk of data leakage. Based on a project reaction theory, objective evaluation from the perspective of a system and an interactive crowdsourcing mode are used for comprehensively and effectively evaluating data sensitivity, and the reaction of a user can be two-dimensional, such as correct or wrong, agreement or disagreement; it may also be multi-dimensional, such as strongly disagreeable, neutral, agreeable, strongly agreeable, and the like. An example of a crowdsourcing task is where Bob shares an Excel on Bob's computer with Alice about Bob's financial status, and the risk rating assessment is very high, normal, low, and very low. After evaluation by the user, we evaluate different contexts. An example of crowd-sourced assessment is that we can be based on a Rasch model, and can effectively reflect the information contribution of items with different characteristics (parameters) when evaluating different tested characteristic levels. The project response function can be defined as
To evaluate the probability, beta, that the nth person has a correct judgment on the item inIndicates the capabilities of the nth person, and δiThe difficulty of the simulation item i. The parameter beta can be estimated by methods such as maximum likelihood estimation and the likenAnd deltai. This requires us to collect enough user responses to different sharing scenarios. Therefore, the data sharing situation and the data sensitivity value are stored for other data sensitivity queries from the client, so that quick response is achieved, and operation is convenient.
In step S1, the context dictionary includes identity information of the sharee and/or sharing policy and/or sharing file type. The context dictionary defines the context of the data to be shared, for example, Bob shares an Excel about Bob's financial status with Alice on Bob's computer. The context that we refine then includes file type, file content type, file storage location, sharer and sharee of the file. FIG. 2 shows a segment of a context map for data. We define a context font as a set of key-value doublets, such as (data type Excel, content type finance, storage location office).
According to the method, different situations in the data sharing process can be accurately described by establishing the situation dictionary, the data privacy leakage risk is evaluated according to the different situations, and corresponding sharing strategies are recommended; the method has the advantages that a crowdsourcing evaluation mode is adopted, the sensitivity of a user to different data privacy is scientifically evaluated by combining a project reaction theory, the data sensitivity values of the user under different scenes are obtained by using collective wisdom and shared wisdom, and more personalized data privacy protection strategies are provided for different purposes; the situation of user data sharing can be sensed, wherein the situation comprises a data category, a content category, a data owner and the like, different data sharing strategies are provided according to different situations, and the privacy of user data is effectively protected; meanwhile, collective intelligence of cloud users is integrated in a crowdsourcing mode, data sensitivity is effectively evaluated based on a project reaction theory, accuracy of data privacy safety risk evaluation is further improved, and data privacy protection service under a cloud data sharing situation is provided for users;
before the context dictionary is sent in step S1, the information of the context dictionary is processed, and the processing mode includes processing in an anonymous form and/or an encrypted form and/or a data obfuscation form. In specific implementation, taking data obfuscation as an example, for instance, context1 (k1 v1, k2 w2, and k3 y3), at a client, after data obfuscation, we form a List [ { v1, v4, v6}, { w1, w2, w5}, { y2, y3, y5} ], where the obfuscation coefficient k is 3, and each context has k3 possible values; the list of possible query context dictionaries is thus a Cartesian product: q { (v1, w1, y2), (v1, w1, y3), (v1, w1, y5), (v1, w2, y2), … }, so that the client sends the query List and Q after data confusion to the server for query.
In specific implementation, after the data sensitivity value obtained in step S2 is returned to the client, the client caches information of the context dictionary corresponding to the data sensitivity value, and provides the client with the information for querying; after the client generates the context dictionary, inquiring corresponding data sensitivity values for the information of the context dictionary in a cache database of the client so that the client can directly obtain the data sensitivity values; if the client side inquires the corresponding data sensitivity value, the information of the situation dictionary is not sent to the server any more; otherwise, if the client does not inquire the corresponding data sensitivity value in the cache database, the step S1 is performed.
In specific implementation, after the data is uploaded to the server according to the selected sharing policy in step S5, the uploaded data is shared.
In specific implementation, in step S3, the data privacy risk is evaluated by using a data privacy risk evaluation function, where the evaluation function may be determined according to different scenarios, such as a weighted product, a product, and the like.
In specific implementation, the sharing policies of different risks in step S4 at least include two policies of completely disclosing shared data and not disclosing shared data; the full disclosure is to directly disclose and share data; the complete disclosure is to encrypt the data and partially hide the metadata of the data. In specific implementation, the security policy value can be between [0,1], and the system can be flexibly adjusted; 0 means completely transparent and 1 means not shared at all. For example, if the user sharing policy is 1, it indicates that the data is not shared at all, which requires encrypting the data. If part of the metadata information is hidden, including data owner information, last modification time, etc., the value may be 0.2.
In a specific implementation, the partial hiding of the metadata of the data includes data owner information and/or last modified time information.
In specific implementation, after the data uploading is completed in step S5 each time, the context information and the corresponding sharing policy of the data uploading sharing are sent to the cloud, and the evaluation history of the time is recorded for the next evaluation and crowd-sourced evaluation analysis.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A context-aware cloud data privacy protection method based on crowd-sourcing evaluation is characterized by comprising the following steps: the method comprises the following steps:
s1, the client generates a context dictionary according to the context information of the data file, and the context dictionary is used for defining the context of the shared data; then the client sends the generated context dictionary to the server as a sensitivity query list;
s2, inquiring and establishing a data sensitivity value: after receiving the client query list, the server queries each situation in the situation list in the data sensitivity value database, and if the situation is evaluated, the server directly returns a data sensitivity value to the client; otherwise, if the situation information is not evaluated, the server end designs the uploaded situation information into a crowdsourcing task, invites the user to evaluate the sensitivity of the data together to obtain a data sensitivity value, and then stores the data sensitivity value obtained after crowdsourcing evaluation into the data sensitivity value database for the server end to inquire;
s3, evaluation of privacy risk exposure value: the server calculates objective probability value of the data privacy leakage event according to the context information received in the step S2 and the leakage probability corresponding to the same previous context information in the server; performing comprehensive evaluation according to the objective probability value and the data sensitivity value obtained by crowdsourcing evaluation in the step S2 to obtain a privacy risk leakage value;
s4, strategy recommendation: forming and recommending a plurality of sharing strategies with different risks according to the privacy risk leakage value evaluated in the step S3;
s5, applying a strategy: and according to the different risk sharing strategies obtained in the step S4, selecting a sharing strategy corresponding to the risk to apply to the data, and sharing the data processed by the corresponding sharing strategy to the cloud.
2. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: in step S1, the context dictionary includes identity information of the sharee and/or sharing policy and/or sharing file type.
3. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: before the context dictionary is sent in step S1, the information of the context dictionary is processed, and the processing mode includes processing in an anonymous form and/or an encrypted form and/or a data obfuscation form.
4. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: after the data sensitivity value obtained in step S2 is returned to the client, the client caches information of the context dictionary corresponding to the data sensitivity value, and provides the client with a query; after the client generates the context dictionary, inquiring corresponding data sensitivity values for the information of the context dictionary in a cache database of the client so that the client can directly obtain the data sensitivity values; if the client side inquires the corresponding data sensitivity value, the information of the situation dictionary is not sent to the server any more; otherwise, if the client does not inquire the corresponding data sensitivity value in the cache database, the step S1 is performed.
5. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: after the data is uploaded to the server according to the selected sharing policy in step S5, the uploaded data is also shared.
6. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: and in the step S3, performing comprehensive evaluation according to the data sensitivity value obtained in the step S2 and the probability value of the occurrence of the data privacy leakage event, wherein the comprehensive evaluation adopts a weighted evaluation function and/or a product evaluation function.
7. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: in step S4, the sharing policies of different risks at least include two policies of completely disclosing shared data and not disclosing shared data; the full disclosure is to directly disclose and share data; the complete disclosure is to encrypt the data and partially hide the metadata of the data.
8. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 7, wherein: partial hiding of metadata of data includes data owner information and/or time information of last modification.
9. The context-aware cloud data privacy protection method based on crowdsourcing evaluation according to claim 1, wherein: after the data uploading is completed in step S5 each time, the context information and the corresponding sharing policy of the data uploading sharing are sent to the cloud, and the evaluation history of this time is recorded for the next evaluation and crowd sourcing evaluation analysis.
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