CN114297714A - Method for data privacy protection and safe search in cloud environment - Google Patents
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Abstract
The invention relates to the technical field of data security, in particular to a method for protecting and searching data privacy in a cloud environment.
Description
Technical Field
The invention relates to the technical field of data security, in particular to a method for protecting data privacy and safely searching in a cloud environment.
Background
With the fusion development of the latest technologies such as cloud computing, the internet of things and the mobile internet, the data volume generated by various industries is increased explosively, the latest report published by the research institution Gartner shows that the volume of the global network equipment in 2016 is increased by 64 hundred million, the volume is increased by 30% in 2015, 550 million new equipment is accessed to the network in 2016 every day on average, and the volume of the network equipment is expected to be increased to 208 hundred million in 2020. The ubiquitous network scale has exploded while also producing a huge amount of multimodal data, and market research organization IDC projected [3], the global amount of data will remain at 50% annual growth rate in the next decade, and projected that this number will reach 40ZB in 2020. In China, with the steady implementation of an 'internet +' action plan, emerging technologies such as the internet of things and the mobile internet are combined with various industries, so that a huge market of big data content consultation services is greatly prized, and big data enters a new stage of leap-type development. Moore's law shows that the improvement of the computing speed of classic computer hardware is close to the physical upper limit, and small and medium-sized enterprises or organizations rely on self-operated servers to complete tasks such as data storage, multi-source fusion and shared computing. First, storage, maintenance, and management of mass data are often very complex and expensive, and may even exceed the load of their existing equipment when involving multi-source data fusion; secondly, because the big data has multiple sources and high dimensionality, potential business knowledge and privacy information can be mined out when the big data is analyzed, and all parties participating in data integration are also the concerns of business and the responsible persons for privacy protection, so that data integration, storage and management are handed over to any business participant and difficult to be questioned, and a manager seeking a proper business unrelated party as data outsourcing service is of great importance to the development of the big data industry. With the development of cloud computing, as a novel data bearing platform, the cloud computing organically integrates data related to each platform and each industry in a network by using a computing resource sharing mode distributed according to needs and the payment characteristic of 'pay-as-you-go', so that the real-meaning big data 'knowledge' mining is realized. Cloud computing has become a dominant tool for solving the problem of big data, and is focused on the essence that the storage and management of data is handled by Cloud Service Providers (CSPs), and the data owner only needs to concentrate on the mining insight of business knowledge. However, as for the twofold nature of swords, in the process of migrating data to a cloud platform, a data owner still has security concerns about data on a package, a data package service (DaaS) mode based on the cloud platform causes classification of data ownership and management rights, and data is stored on which servers is transparent to the data owner, which determines the necessity of data privacy protection in a cloud environment. The recent data leakage incident [136] occurring in commercial macros such as Google, Kiddicare, etc. has exacerbated the concern of DaaS models. In addition, facing mass generated data, strong data association analysis, and a brand-new data publishing architecture, the privacy protection technology also faces the following challenges:
firstly, the characteristics of diversified data sources, fragmented information, individualized and differentiated privacy requirements result in the risk of privacy disclosure when multi-party data are fused. On one hand, privacy protection strategies and granularity adopted by data owners for the data are different, when the data are fused, the phenomena of over-protection, under-protection and the like of the data can be caused by the partial order relation between the strategies and the correlation between data attributes, and how to detect and resolve conflicts among privacy operations of all parties becomes a practical problem in maximizing the usability of the fused data while balancing the privacy protection requirements of all parties; on the other hand, in the process of data fusion, each party also prevents other participants from acquiring private information of the other party, so that designing a multi-party data security fusion strategy cannot be avoided.
Secondly, due to the characteristics of multi-source, high-dimensional, batch addition and the like of the large data, the traditional privacy protection method for single data source release at one time is insufficient, and the problem of non-explicit privacy information leakage caused by data depth correlation analysis cannot be effectively solved. For example, in order to prevent the disclosure of the privacy of the user, before data fusion and release, each data source platform needs to perform desensitization processing (such as perturbation, noise addition, generalization, and the like) on the data set of each platform, but the local privacy protection on each source data cannot avoid the risk of the disclosure of the global data privacy after fusion, which is also an essential problem facing the privacy protection of large data release.
Thirdly, from the multidimensional requirements of the data owner and the user on the large data view, on one hand, the data owner needs the large data to customize the result view of 'thousands of people and thousands of faces' according to the role and authority of the user due to privacy concerns, and on the other hand, the data user expects more to obtain the rapid search experience meeting the precision requirement than waiting for an accurate solution silently. From the search mode, the traditional search engine technology is a scanning type search based on the existence of keywords, and the mode cannot meet the requirement of quick and high-precision search proposed by users when the users face large data with 4V characteristics. From a measurement evaluation system, most of the existing measurement indexes only aim at single-dimension evaluation, and the application requirements of big data on multi-dimensional attributes such as search timeliness, precision, privacy and the like are difficult to meet. These problems have led researchers to explore new fast and accurate data search and privacy protection technologies corresponding thereto.
Therefore, it is necessary to solve the security problem and the data search problem of cloud data.
Disclosure of Invention
The invention provides a method for protecting data privacy and safely searching in a cloud environment aiming at the problems in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention provides a method for protecting data privacy and safely searching in a cloud environment, which comprises the following steps:
performing multiple rounds of thinning anonymization algorithm on original data after anonymization processing of each cloud tenant to obtain a data set, and uploading the data set to a cloud end through multiple rounds of collaborative thinning of the data set by the cloud tenant with the attribute of maximum information acquisition gain under the condition of meeting data anonymity, wherein the cloud tenant gives a final controller of the data set to a cloud service provider;
the cloud service provider encrypts the received data, sends the encrypted data to the public cloud end, stores the received data according to the number and the size of the data blocks and authorizes the inquiry authority to the private user;
step three, the private user puts forward a data search requirement and submits the data search requirement to a search engine Hermes, the search engine Hermes judges whether a search request put forward by the user exceeds the search precision upper limit of the authorized query authority of the user, if so, the search engine Hermes refuses to accept the search request put forward by the user, and if not, the search request put forward by the user is accepted;
step four, generating a search task J after receiving the request, inquiring corresponding data according to the search task J by the public cloud, and sending the matched encrypted file to an authorized user;
and step five, the search engine Hermes receives the matched encrypted file and returns the encrypted file to the authorized user, and the authorized user decrypts the encrypted file at the terminal of the authorized user.
Preferably, in the first step, the multi-round refinement anonymization algorithm includes that each cloud tenant with data fusion calculates information entropy of each attribute according to local data owned by the cloud tenant and publishes a maximum entropy value for comparison, each party selects the attribute with the maximum global entropy value in the current round, the owner of the attribute performs refinement division on the attribute based on the data division result in the previous round, if the division result does not violate data anonymity constraint, the division result is published, otherwise, the next round is directly performed until no attribute can contribute to data refinement division on the premise of meeting anonymity constraint.
Preferably, in the step one, the reputation grade set by the cloud tenant on the cloud service provider hides the association relation between the data for the cloud service provider with the semi-trusted reputation grade, and ensures the value range balanced distribution of the attributes in a grouping and balancing manner, so as to prevent the cloud service provider from revealing the data privacy of the cloud tenant; and (4) providing a classification index tree data structure for the cloud service provider with the completely untrusted reputation level, and verifying the correctness and integrity of data returned by the cloud service provider.
Preferably, in the second step, when the authorization query right is performed on the private user, different grades can be classified according to the role of the private user, the data access granularity and the payment capability, and a high-grade authorized user can access more data information, whereas a low-grade authorized user can access limited data information.
Preferably, the public cloud queries corresponding data according to the search task J and performs data query according to a white lift method, wherein the white lift method comprises the following steps: step A, setting a user search request under a Hadoop architecture as a triple Q (Op, D, rho), wherein Op represents search operation of a user on a target data set D, and rho is a search precision lower limit value set by the user;
b, extracting an initial sample S from the data set D, and then performing m times of repeated sampling { S1., Sm } by taking S as a domain of discourse;
step C, performing approximate calculation on m results { Op (S1) }, Op (Sm) } generated by implementing operation Op (D) in the step B to obtain a relative error value of the variation coefficient;
and step four, evaluating according to the relative error of the variation coefficient in the step C to obtain a search result meeting the approximate precision of the user.
Preferably, when an attacker directly accesses the public cloud, fuzzy query logic is adopted, and the search domain space and the semantic space of the keywords are expanded through the search index based on granularity, so that the attacker cannot accurately deduce the file content.
Preferably, the search engine Hermes comprises a search evaluation module, an approximate search module and a search maintenance module.
Preferably, the search evaluation module is responsible for bridging the user and the data platform, waiting for the search request of the user and analyzing the required resources; intermittently collecting state information of a data platform to finally form a feasible search plan, wherein the approximate search module comprises a sampling layer, an acceleration layer and an operation layer, the operation layer consists of a plurality of basic operation components, and for a given operation Op, if an unbiased estimation that corresponding statistics formed based on a specific data sampling algorithm are Op exists, the Op can be brought into the operation layer in the form of the components; the acceleration layer provides a quick response mechanism, and records related information of historical search by constructing a search record TLB table so as to accelerate the received isomorphic search; the sampling layer provides various sampling technologies, the various sampling technologies comprise Bernoulli sampling, a bootstrap method and a cutting method, the search maintenance module introduces an increment sampling strategy, for isomorphic search with variable precision, time overhead is greatly reduced by effectively multiplexing historical results, and for the characteristic of incremental release of large data, the search maintenance module starts from the granularity of privacy protection and the stability of the historical results and provides the applicability of the historical results relative to isomorphic search of new version data.
The invention has the beneficial effects that:
the invention provides a method for protecting data privacy and safely searching in a cloud environment, which comprises the following steps: performing multiple rounds of thinning anonymization algorithm on original data after anonymization processing of each cloud tenant to obtain a data set, and uploading the data set to a cloud end through multiple rounds of collaborative thinning of the data set by the cloud tenant with the attribute of maximum information acquisition gain under the condition of meeting data anonymity, wherein the cloud tenant gives a final controller of the data set to a cloud service provider; the cloud service provider encrypts the received data, sends the encrypted data to the public cloud end, stores the received data according to the number and the size of the data blocks and authorizes the inquiry authority to the private user; step three, the private user puts forward a data search requirement and submits the data search requirement to a search engine Hermes, the search engine Hermes judges whether a search request put forward by the user exceeds the search precision upper limit of the authorized query authority of the user, if so, the search engine Hermes refuses to accept the search request put forward by the user, and if not, the search request put forward by the user is accepted; step four, generating a search task J after receiving the request, inquiring corresponding data according to the search task J by the public cloud, and sending the matched encrypted file to an authorized user; and step five, the search engine Hermes receives the matched encrypted file and returns the encrypted file to the authorized user, and the authorized user decrypts the encrypted file at the terminal of the authorized user.
Drawings
FIG. 1 is a block diagram of the framework of the present invention.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention. The present invention is described in detail below with reference to the attached drawings.
As shown in fig. 1, the method for protecting data privacy and safely searching in a cloud environment provided by the present invention includes the following steps: performing multiple rounds of thinning anonymization algorithm on original data after anonymization processing of each cloud tenant to obtain a data set, and uploading the data set to a cloud end through multiple rounds of collaborative thinning of the data set by the cloud tenant with the attribute of maximum information acquisition gain under the condition of meeting data anonymity, wherein the cloud tenant gives a final controller of the data set to a cloud service provider; the cloud service provider encrypts the received data, sends the encrypted data to the public cloud end, stores the received data according to the number and the size of the data blocks and authorizes the inquiry authority to the private user; step three, the private user puts forward a data search requirement and submits the data search requirement to a search engine Hermes, the search engine Hermes judges whether a search request put forward by the user exceeds the search precision upper limit of the authorized query authority of the user, if so, the search engine Hermes refuses to accept the search request put forward by the user, and if not, the search request put forward by the user is accepted; step four, generating a search task J after receiving the request, inquiring corresponding data according to the search task J by the public cloud, and sending the matched encrypted file to an authorized user; and step five, the search engine Hermes receives the matched encrypted file and returns the encrypted file to the authorized user, and the authorized user decrypts the encrypted file at the terminal of the authorized user.
In this embodiment, in the first step, the multi-round refinement anonymization algorithm includes that each cloud tenant with data fusion calculates information entropy of each attribute with respect to local data owned by the cloud tenant and publishes a maximum entropy value for comparison, each party selects an attribute with a maximum global entropy value in the current round, an owner of the attribute performs refinement division on the attribute based on a data division result in the previous round, if the division result does not violate anonymous data constraint, the division result is published, otherwise, the next round is directly performed, until no attribute can contribute to data refinement division on the premise that anonymous constraint is satisfied.
In the embodiment, step one, the reputation grade set by the cloud tenant on the cloud service provider hides the association relation between the data for the cloud service provider with the semi-credible reputation grade, and ensures the value range balanced distribution of the attributes in a grouping and balancing manner, so as to prevent the cloud service provider from revealing the data privacy of the cloud tenant; and (4) providing a classification index tree data structure for the cloud service provider with the completely untrusted reputation level, and verifying the correctness and integrity of data returned by the cloud service provider.
In this embodiment, in the second step, when the authorization query right is performed on the private user, different levels of division may be performed according to the role of the private user, the data access granularity, and the payment capability, and the high-level authorized user may access more data information, whereas the low-level authorized user may access limited data information.
In this embodiment, the public cloud queries corresponding data according to the search task J and performs data query according to a white lift method, where the white lift method includes the following steps: step A, setting a user search request under a Hadoop architecture as a triple Q (Op, D, rho), wherein Op represents search operation of a user on a target data set D, and rho is a search precision lower limit value set by the user; b, extracting an initial sample S from the data set D, and then performing m times of repeated sampling { S1., Sm } by taking S as a domain of discourse; step C, performing approximate calculation on m results { Op (S1) }, Op (Sm) } generated by implementing operation Op (D) in the step B to obtain a relative error value of the variation coefficient; and step four, evaluating according to the relative error of the variation coefficient in the step C to obtain a search result meeting the approximate precision of the user.
In this embodiment, when an attacker directly accesses the public cloud, the search domain space and the semantic space of the keywords are expanded by using the search index based on the granularity by using the fuzzy query logic, so that the attacker cannot accurately infer the file content.
In this embodiment, the search engine Hermes includes a search evaluation module, an approximate search module, and a search maintenance module.
In this embodiment, the search evaluation module is responsible for bridging the user and the data platform, waiting for the search request of the user and analyzing the resources required by the user; intermittently collecting state information of a data platform to finally form a feasible search plan, wherein the approximate search module comprises a sampling layer, an acceleration layer and an operation layer, the operation layer consists of a plurality of basic operation components, and for a given operation Op, if an unbiased estimation that corresponding statistics formed based on a specific data sampling algorithm are Op exists, the Op can be brought into the operation layer in the form of the components; the acceleration layer provides a quick response mechanism, and records related information of historical search by constructing a search record TLB table so as to accelerate the received isomorphic search; the sampling layer provides various sampling technologies, the various sampling technologies comprise Bernoulli sampling, a bootstrap method and a cutting method, the search maintenance module introduces an increment sampling strategy, for isomorphic search with variable precision, time overhead is greatly reduced by effectively multiplexing historical results, and for the characteristic of incremental release of large data, the search maintenance module starts from the granularity of privacy protection and the stability of the historical results and provides the applicability of the historical results relative to isomorphic search of new version data.
Although the present invention has been described with reference to the above preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for data privacy protection and safe search in a cloud environment is characterized by comprising the following steps:
performing multiple rounds of thinning anonymization algorithm on original data after anonymization processing of each cloud tenant to obtain a data set, and uploading the data set to a cloud end through multiple rounds of collaborative thinning of the data set by the cloud tenant with the attribute of maximum information acquisition gain under the condition of meeting data anonymity, wherein the cloud tenant gives a final controller of the data set to a cloud service provider;
the cloud service provider encrypts the received data, sends the encrypted data to the public cloud end, stores the received data according to the number and the size of the data blocks and authorizes the inquiry authority to the private user;
step three, the private user puts forward a data search requirement and submits the data search requirement to a search engine Hermes, the search engine Hermes judges whether a search request put forward by the user exceeds the search precision upper limit of the authorized query authority of the user, if so, the search engine Hermes refuses to accept the search request put forward by the user, and if not, the search request put forward by the user is accepted;
step four, generating a search task J after receiving the request, inquiring corresponding data according to the search task J by the public cloud, and sending the matched encrypted file to an authorized user;
and step five, the search engine Hermes receives the matched encrypted file and returns the encrypted file to the authorized user, and the authorized user decrypts the encrypted file at the terminal of the authorized user.
2. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: in the first step, the multi-round refinement anonymization algorithm comprises the steps that each cloud tenant with data fusion calculates the information entropy of each attribute according to local data owned by the cloud tenant and publishes the maximum entropy value for comparison, each party selects the attribute with the maximum global entropy value in the current round, the owner of the attribute performs refinement division on the attribute based on the data division result in the previous round, if the division result does not violate the anonymity constraint of the data, the division result is published, otherwise, the next round is directly performed until no attribute performance contributes to the refinement division of the data on the premise of meeting the anonymity constraint.
3. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: step one, a cloud tenant sets a credit level for a cloud service provider, and for the cloud service provider with a semi-trusted credit level, the incidence relation between data is hidden, and through a grouping equalization mode, the value range balanced distribution of attributes is ensured, and the cloud service provider is prevented from revealing the data privacy of the cloud tenant; and (4) providing a classification index tree data structure for the cloud service provider with the completely untrusted reputation level, and verifying the correctness and integrity of data returned by the cloud service provider.
4. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: in the second step, when the authorization inquiry authority is carried out on the private user, different grades can be divided according to the role of the private user, the data access granularity and the payment capacity of the private user, the high-grade authorized user can access more data information, and otherwise, the low-grade authorized user can access limited data information.
5. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: the public cloud end queries corresponding data according to the search task J and performs data query according to a white lift method, wherein the white lift method comprises the following steps: step A, setting a user search request under a Hadoop architecture as a triple Q (Op, D, rho), wherein Op represents search operation of a user on a target data set D, and rho is a search precision lower limit value set by the user; b, extracting an initial sample S from the data set D, and then performing m times of repeated sampling { S1., Sm } by taking S as a domain of discourse;
step C, performing approximate calculation on m results { Op (S1) }, Op (Sm) } generated by implementing operation Op (D) in the step B to obtain a relative error value of the variation coefficient; and step four, evaluating according to the relative error of the variation coefficient in the step C to obtain a search result meeting the approximate precision of the user.
6. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: when an attacker directly accesses the public cloud, fuzzy query logic is adopted, and a search domain space and a semantic space of keywords are expanded through a search index based on granularity, so that the attacker cannot accurately deduce the file content.
7. The method for protecting data privacy and safely searching in the cloud environment according to claim 1, wherein: the search engine Hermes comprises a search evaluation module, an approximate search module and a search maintenance module.
8. The method for data privacy protection and secure search in the cloud environment according to claim 7, wherein: the search evaluation module is responsible for bridging the user and the data platform, waiting for the search request of the user and analyzing the required resources; intermittently collecting state information of a data platform to finally form a feasible search plan, wherein the approximate search module comprises a sampling layer, an acceleration layer and an operation layer, the operation layer consists of a plurality of basic operation components, and for a given operation Op, if an unbiased estimation that corresponding statistics formed based on a specific data sampling algorithm are Op exists, the Op can be brought into the operation layer in the form of the components; the acceleration layer provides a quick response mechanism, and records related information of historical search by constructing a search record TLB table so as to accelerate the received isomorphic search; the sampling layer provides various sampling technologies, the various sampling technologies comprise Bernoulli sampling, a bootstrap method and a cutting method, the search maintenance module introduces an increment sampling strategy, for isomorphic search with variable precision, time overhead is greatly reduced by effectively multiplexing historical results, and for the characteristic of incremental release of large data, the search maintenance module starts from the granularity of privacy protection and the stability of the historical results and provides the applicability of the historical results relative to isomorphic search of new version data.
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