Image storage across distributed computing systems

Provided is a method, system, and computer program product for storing images across multiple distributed computing systems according to image sensitivity. The method comprises identifying an image and analyzing the image to identify sensitive information in the image. The method further comprises splitting the image into a sensitive portion and a non-sensitive portion. The method further comprises storing the sensitive portion of the image in a first distributed computing system and storing the non-sensitive portion of the image in a second distributed computing system.

BACKGROUND

The present disclosure relates generally to the field of computing, and more particularly to storing images across multiple distributed computing systems according to image sensitivity.

Organizations generate and store large amounts of data, with the amount of data being generating and stored continuing to increase. As such, the costs of storing the data is also increasing. In the past, this meant storing all of the data in servers owned and operated by the organization. However, the cost of building sufficient onsite storage (e.g., CapEx costs), maintaining it on-premise, support costs to the vendor, skills required to manage it, and the cost of maintaining it over time are important factors to consider. Many organizations cannot afford the cost of storing all of their data on-premise. As such, organizations have turned to cloud-based storage solutions.

SUMMARY

Embodiments of the present disclosure include a method, computer program product, and system for storing images across multiple distributed computing systems according to image sensitivity. The method comprises identifying an image and analyzing the image to identify sensitive information in the image. The method further comprises splitting the image into a sensitive portion and a non-sensitive portion. The method further comprises storing the sensitive portion of the image in a first distributed computing system and storing the non-sensitive portion of the image in a second distributed computing system. In some optional embodiments, the method further comprises identifying boundary data and location data for the sensitive and non-sensitive portions and storing the location and boundary data in a database.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field of computing, and in particular to storing images across multiple distributed computing systems according to image sensitivity. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Organizations generate and store large amounts of data, with the amount of data being generating and stored continuing to increase. As such, the costs of storing the data is also increasing. In the past, this meant storing all of the data in servers owned and operated by the organization. However, the cost of building sufficient onsite storage (e.g., CapEx costs), maintaining it on-premise, support costs to the vendor, skills required to manage it, and the cost of maintaining it over time are important factors to consider. Many organizations cannot afford the cost of storing all of their data on-premise. As such, organizations have turned to cloud-based storage solutions. However, the cheaper cloud-based solutions may not meet the security requirements of the business when it comes to their most sensitive data. To balance costs and security, organizations may keep their sensitive data within the organization (e.g., on-premise or in a more secure cloud) and their non-sensitive data in a public cloud that provides cheap, scalable storage.

One type of data that many organizations generate is image data, with some organizations generating a large number of images that have to be kept by the organization. When compared to other types of data, storing images requires a large amount of computer storage space. Depending on the industry, the number of images, the amount of time, and the type of storage that is required varies. For example, in the case of financial companies, many of the scanned documents are stored for years (in some cases 7 years or more) as part of regulatory requirements. Furthermore, as techniques to derive information/insights from unstructured data gets more popular, organizations need to store these images for a longer amount of time to fully extract the value from the images.

However, images often contain sensitive information, such as personally identifiable information, medical information, financial information, etc. This is especially true in certain industries, such as the financial or healthcare industries. For example, scanned copies of account opening forms, know your customer (KYC) information in the banking sector, invoices and other financial documents in all industries, images in the healthcare industry, audit pictures of sensitive areas of any organization, and traffic violation related pictures may contain sensitive content and be subject to strict regulatory rules regarding their storage. Even if the sensitive data is only a small portion of the image, these images may subject to strict data privacy and protection rules, and therefore require a much more expensive off-premise storage solution that guarantees the security of the data or even on-premise storage. Compliance with these requirements by storing any image that contains such sensitive data can be extremely expensive.

Embodiments of the present disclosure may address the above and other problems with current solutions by enabling images with sensitive information to be stored across different distributed computing environments. This may be achieved by analyzing each image and identifying the sensitive and non-sensitive portions of the image based on the enterprise data and compliance policies. The images are then split, the sensitive portions of the image are then stored in sensitive data clusters (e.g., within on-premise storage, in secured off-premise storage, etc.), and the non-sensitive portions of the image are stored at inexpensive storages in cloud clusters. Accordingly, the amount of image data that has to be stored on-premise (or in the most expensive, secure off-premise locations) can be significantly reduced.

In some embodiments, the system may update an “image sensitivity database’ with the locations and boundary information of the sensitive and non-sensitive portions of image when splitting and storing the image. The location information may be used to subsequently retrieve the portions of the image so that they can be recombined when requested, and the boundary information may be used by the system to determine how the portions should be recombined (e.g., which edges of the different portions should be stitched together, where each portion belongs in the final image, etc.). Additionally, the system may store the image's hash value and other attributes and relations in the enterprise context. The hash value may be used both to identify the image, and to identify any identical images that have been stored. If an identical image has already been storage, the system may create a link to the existing record for the identical image. Accordingly, the hash value may also be used in deduplication of images and avoiding storage. The hash value may be generated for the image using known image hashing processes.

Embodiments further include methods, systems, and computer program products for recombining the sensitive and non-sensitive portions of the image in response to a request for the complete image. When a user requests the image, the system identifies the locations of the various portions of image spread across different on-premise and off-premise locations. The system may use the image sensitivity database to identify the location of the various portions of the image. The system may search the image sensitivity database using the hash value of the image and retrieve the corresponding location and boundary information. For example, when the image was originally split into sensitive and non-sensitive portions and stored, the system may have saved the hash value for the image and the location of each portion in the image sensitive database, along with any information necessary to recombine the portions (the boundary information). The system may retrieve the portions using the location data from the image sensitivity database, combine the image portions to make the original image, and provide the complete image to user.

It is to be understood that the aforementioned advantages are example advantages and should not be construed as limiting. Embodiments of the present disclosure can contain all, some, or none of the aforementioned advantages while remaining within the spirit and scope of the present disclosure.

Turning now to the figures,FIG.1illustrates a block diagram of an example computing environment100in which illustrative embodiments of the present disclosure may be implemented. The computing environment100includes a remote device110that is communicatively coupled to a hybrid cloud120via a network150. The computing environment100may further include one or more other remote devices140. Each of the other remote devices140may, in some embodiments, be substantially similar to, or the same as, remote device110.

Consistent with various embodiments, the remote device110may be a computer system. The remote device110may include one or more processors114and one or more memories116. The one or more memories116may contain one or more images118and one or more applications170. The remote device110may be configured to communicate with the hybrid cloud120through an internal or external network interface112. The network interface112may be, for example, a modem or a network interface card. The remote device110may be equipped with a display or monitor. Additionally, the remote device110may include optional input devices (e.g., a keyboard, mouse, scanner, or other input device), and/or any commercially available or custom software (e.g., browser software, communications software, server software, natural language processing software, search engine and/or web crawling software, filter modules for filtering content based upon predefined parameters, etc.). In some embodiments, the remote device110may be a server, desktop, laptop, or hand-held device.

The hybrid cloud120may include one or more private (e.g., on-premise) clouds122and one or more public (e.g., off-premise) clouds126. The hybrid cloud120may further include an image manager130. Each of the private and public clouds122,126may include a plurality of nodes160. The nodes160may include any types of nodes, including compute nodes, management nodes, and storage nodes. Additionally, the private and public clouds122,126may each include one or more image stores124,128, respectively. The private and public clouds122,126may be configured to communicate with each other.

The image manager130may be configured to process requests received from the remote device110. For example, the image manager130may be configured to receive a request for an image, authenticate the request (e.g., ensure that the requestor has permission to receive the image), and process the request. In some embodiments, processing the request may include determining whether the requested image is stored as a complete image (e.g., in either the private cloud image store124or the public cloud image store128) or whether the image have been split. This may be done by performing a lookup operation on an image sensitivity database to retrieve a record for the image using information contained in the request (e.g., a hash value of the image, a name of the image, etc.). Upon receiving the record for the image, the image manager130may determine whether the image has been split into multiple portions or not. For example, the image manager130may determine whether the image has been split by looking for a flag in the record.

In embodiments where the image has been split, the image manager130may further determine the locations of each portion of the image and retrieve the boundary information from the record. The location information in the record may indicate where in the private cloud image store124the sensitive portions of the image are stored and where in the public cloud image store128the non-sensitive portions of the image are stored. The image manager130may then retrieve both the sensitive portion and the non-sensitive portion of the image from the image stores124,128. Using the boundary information, the image manager130may merge, stitch, or otherwise combine the sensitive portion and the non-sensitive portion of the image to form a complete (combined) image. The image manager130may then return the complete image to the remote device110.

In some embodiments, the image manager130may be further configured to analyze images stored in the private cloud image store124and determine whether they contain sensitive data. Images that don't contain sensitive data may be moved to the public cloud image store128to reduce costs. Furthermore, the image manager130may be configured to determine whether images that do include sensitive data can be split up and stored across different distributed computing systems (e.g., in both the private cloud122and the public cloud126). If an image contains sensitive data but cannot be split up, the image may be left in its complete form in the private cloud image store124. For example, images that are entirely or almost entirely (e.g., above a threshold) made up of sensitive data may benefit very little from being split up, and as such may be left in the private cloud image store124. Additionally, some images may contain information that is so sensitive that the image manager130determines they cannot be split up under any circumstances. This may be based on, for example, user or organization preferences and/or security and compliance regulations. These images would also be kept in the private cloud image store124.

If the image manager130determines that an image in the private cloud image store124can be split up, the image manager130may first determine whether the image has already been split up. To do this, the image manager130may compare a hash value of the image to hash values of images recorded in the image sensitivity database. If the image manager130finds a match, which indicates that the image has already been split up and stored across the distributed computing systems, the image manager130may instead create a link to the existing record for the image, and then delete the image from the private cloud image store124.

On the other hand, if the image manager determines that the image has not yet been split up, the image manager130may retrieve the image and analyze the image using compliance policies and regulations to determine which parts of the image are sensitive (i.e., contain sensitive data) and which parts are not sensitive (i.e., do not contain sensitive data). The image manager130may use known image processing techniques to determine whether parts of the image are sensitive, as would be recognized by persons of ordinary skill in the art.

The image manager130may then split the image into sensitive and non-sensitive portions, store the sensitive portion back in the private cloud image store124, and store the non-sensitive portion in the public cloud image store128. Additionally, the image manager130may update a record in the image sensitivity database (not shown) to indicate the locations of the sensitive and non-sensitive portions in the respective image stores124,128, as well as the boundary information (i.e., the information necessary for recombining the image portions into the complete image). The image manager130may also store a hash value of the complete image in the image sensitivity database, as necessary, and set a flag in the record for the image that indicates that the image has been split. The image manager130may split the image into sensitive and non-sensitive portions using any suitable image processing techniques, as would be recognized by persons of ordinary skill in the art.

While the image manager130is shown as being inside the hybrid cloud120, but not inside either the private cloud122or the public cloud126, this is done for illustrative purposes. In some embodiments, the image manager130could be in either the private cloud122or the public cloud126, and in some embodiments the image manager130(or multiple image managers130) may be in both of the private cloud122and the public cloud126. Additionally, the image manager130may be part of the hybrid cloud communication/management fabric that allows the private cloud122and the public cloud126to interact, share data and applications, etc.

The remote device110and the image manager130may be distant from each other and communicate over a network150. In some embodiments, the image manager130may be a central hub from which remote devices110can establish a communication connection, such as in a client-server networking model. Alternatively, the remote device110and the image manager130may be configured in any other suitable networking relationship (e.g., in a peer-to-peer configuration or using any other network topology).

In some embodiments, the network150can be implemented using any number of any suitable communications media. For example, the network150may be a wide area network (WAN), a local area network (LAN), an internet, or an intranet. In certain embodiments, the remote device110and the image manager130may be local to each other and communicate via any appropriate local communication medium. For example, the remote device110and the image manager130may communicate using a local area network (LAN), one or more hardwire connections, a wireless link or router, or an intranet. In some embodiments, the remote device110and the image manager130may be communicatively coupled using a combination of one or more networks and/or one or more local connections. For example, the image manager130may be hardwired to the remote device110(e.g., connected with an Ethernet cable) while other remote devices140may communicate with the image manager130using the network150(e.g., over the Internet).

In some embodiments, the network150can be implemented within a cloud computing environment, or using one or more cloud computing services. For example, the network150may be part of the hybrid cloud120. Consistent with various embodiments, a cloud computing environment may include a network-based, distributed data processing system that provides one or more cloud computing services. Further, a cloud computing environment may include many computers (e.g., hundreds or thousands of computers or more) disposed within one or more data centers and configured to share resources over the network150. In some embodiments, the network150may be substantially similar to, the same as, or part of the cloud computing environment50described with respect toFIG.6.

FIG.1is intended to depict the representative major components of image processing environment100. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG.1, components other than or in addition to those shown inFIG.1may be present, and the number, type, and configuration of such components may vary. Likewise, one or more components shown within the image processing environment100may not be present, and the arrangement of components may vary.

For example, whileFIG.1illustrates an example image processing environment100having an image manager130and three remote devices110,140that are communicatively coupled via a single network150, suitable network architectures for implementing embodiments of this disclosure may include any number of host devices, image managers, public clouds, private clouds, remote devices, and networks. The various models, modules, systems, and components illustrated inFIG.1may exist, if at all, across a plurality of host devices, remote devices, and networks.

Referring now toFIG.2, illustrated is a block diagram of an image processing system200for storing portions of images in different distributed computing systems based on the sensitivity of content in the images, in accordance with embodiments of the present disclosure. The image processing system200may include, be substantially similar to, or be the same as, the image manager130discussed above with respect toFIG.1. In some embodiments, the image processing system200includes a feature extraction subsystem210, a sensitivity analysis and processing subsystem220, a storage manager240(e.g., a storage controller), data compliance policies and regulations252, and an image sensitivity database254.

The feature extraction subsystem210may be configured to retrieve (or receive) images from the image store202. The image store202may be in a hybrid cloud environment (e.g., private cloud image store124or public cloud image store128), or it may be in an external system (e.g., remote device110). After retrieving the image, the feature extraction subsystem210utilizes existing object detection and text extraction techniques to extract the objects and text present in the image. For example, the feature extraction subsystem210may include an image analysis module212that is configured to analyze the image and determine portions of the image that contain recognizable objects and text. Then, the object extraction module214and the text extraction module216may use the output from the image analysis module212to analyze the portions of the image having objects and text to extract the various objects and text. The extracted features from the image may then be sent by the feature extraction subsystem210to the sensitivity analysis and processing subsystem220for further processing.

As an illustrative example, the image may be a patient's medical record, such as a medical text report. As such, the image may contain text (e.g., the patient's name, age, or any other attribute) and one or more objects (e.g., scan results). The feature extraction subsystem210may analyze the received image to identify and extract the text and objects from the image, and then send the extracted features, along with the image itself, to the sensitivity analysis and processing subsystem220.

The sensitivity analysis and processing subsystem220may be configured to analyze the received images using the extracted features and the data compliance policies and regulations252to determine how to split the image into two or more portions. In other words, given the objects and text present in the image, sensitive portions of the image are detected using the rules fed to the system. This may be done using a sensitivity identifier224that analyzes the extracted features (e.g., the objects and text) using the data compliance policies and regulations252. Individual text, objects, or the combination of objects and text is analyzed to identify the sensitivity in the specified context using the rules specified in the data compliance policies and regulations252.

The data compliance policies and regulations252may be specific to the type of data where the system200is being deployed. For example, some systems200may contain financial rules, while other systems may contain rules related to healthcare data. An example of a rule that may be included in the data compliance policies and regulations252for a healthcare provider is that name or age of a patient may be considered sensitive. As another example, a combination of data may need to be included in the image (e.g., a name combined with the scan result) for the sensitivity identifier224to flag the data as sensitive. In yet another example, medical scan results may be considered sensitive data, even if they are not accompanied by a name or other identifier for the patient.

In some embodiments, the system is bootstrapped with the rules learned from an organization's polices. These rules can be auto-learned from the policy documents using the AI module/context analyzer222or manually derived by subject matter experts. Further, the rules can be updated over time based on the accuracy of the rule system. Periodically, a provision can be kept for human intervention. The output of the rule system can be validated by a human, and corrections are fed back to update the rules. The iterative process to update the rules help in not missing the sensitive data from an image, and also new rules can be added to the system with time.

In some embodiments, the AI module/context analyzer222may be configured to analyze the information received from the feature extract subsystem210to provide additional context. For example, text that may not seem sensitive in some contexts may actually be very sensitive in other contexts, and the AI module/context analyzer may be configured to identify the context in which the extracted features exist. The sensitivity identifier224may utilize this contextual information to help with the classification of the sensitivity of the data in the image.

Once the sensitivity is determined, the sensitivity identifier224may add a label to the image that identifies the sensitivity of the entire image. The label may be based on, for example, the most sensitive information included in the image. This may be done to ensure that images having data of different sensitivities are treated, by default, in accordance with the policies for the most sensitive data in the image.

The sensitivity analysis and processing subsystem220may be configured to split the image into two or more portions based on the sensitivity of the image. For example, an image that contains some sensitive data and some non-sensitive data may be split into two portions: one that includes the sensitive data, and one that includes only the non-sensitive data. Location and boundary of the sensitive information (e.g., objects) is also identified by the image portions location identifier226using thresholding, classification, and segmentation techniques.

The information related to the sensitive and non-sensitive image portions are stored in the image sensitivity database254. In some embodiments, the image sensitivity database254contains a record for each image with the image name, hash value, associated application, image metadata, location and boundary information about sensitive portions of data and non-sensitive portions of data, and the stored location of the sensitive and non-sensitive portions of the image. As discussed herein, the image hash value may be stored in the image sensitivity database254so that the same steps don't need to be repeated again and again for the same images, and the record for the image will be linked to the existing similar record within the image sensitivity database254instead.

After identifying the sensitive portion(s) of the image, the sensitive portion(s) are taken out of the image and moved to a secure storage environment. The secure storage environment may be on-premise storages (e.g., on-premise storage cluster244) or eligible sensitive clusters within the cloud. This may be done by the storage manager240. Furthermore, the location of where the sensitive portion is stored is updated in the image sensitivity database254by the storage location identifier228. Similarly, the storage manager240moves the non-sensitive portion of the image to cloud storage (e.g., cloud storage cluster242). The storage location identifier228then stores the non-sensitive portion's location in the image sensitivity database254.

In some embodiments, the system200monitors health check parameters such as firewall configuration, unauthorized access, and any other activity which might indicate potential misuse of the data on the sensitive clusters. Further, a health score is maintained for each sensitive cluster based on the activities which might have been performed. If the health score falls below a threshold, a sensitive cluster is automatically removed by the system200from the list of sensitive clusters.

Furthermore, each enterprise may have data clusters within their own multiple data centers, private clouds hosted at vendor data clusters, and multiple public cloud data clusters located across the world. Also, it has a list of data centers (and data clusters it carries) with attributes associated to the data that it can carry based on the sensitivity of the data. The system200may maintain a data sensitivity level of each cluster and data cluster based on the sensitivity, security, privacy, industry and domain specific certifications the data cluster has, the country in which the data cluster is located, the country in which the enterprise operates under the jurisdiction of, etc. The system200may utilize this list of information to determine where data can be stored. In many cases, it will be on-premise for the regulated industries.

In some embodiments, the system200may be further configured to respond to user260(or application) requests for an image. When the user260or remote system requests the image, the image sensitivity database254is checked based on the image's attributes stored in image sensitivity database254. The entry for the particular image is retrieved, including the location of the sensitive and non-sensitive portions of the image. Both portions of image are collected and merged by the image portion merger230using the location of each portion of image from the image sensitivity database254. The portions may be merged using any suitable technique for stitching or combining images, as would be recognized by persons of ordinary skill in the art. After merging the image into a complete image, the image is returned to the user260or system that requested it.

The above way of stitching the image first and then displaying to the end user might result in some delay, which may be fine for some applications. For the applications where the extra delay is not tolerable, stitching may not be performed. Instead all the portions of the image are sent to the front end (where the image is going to be displayed), and the front end system can start showing the image portions while stitching at the same time

Referring now toFIG.3, illustrated is a flowchart of an example method300for splitting an image into multiple portions based on image sensitivity and storing the portions of images in different distributed computing systems, in accordance with embodiments of the present disclosure. The method300may be performed by hardware, firmware, software executing on a processor, or any combination thereof. For example, the method300may be performed by a processor (e.g., in a server in a hybrid cloud120). The method300begins at operation302, wherein the processor identifies an image to be stored.

In some embodiments, the image may be identified shortly after it has been created. For example, a system may be set up to automatically review all images generated by an organization to determine whether it contains sensitive information and, if so, if it can be split. In other embodiments, the image may be identified as part of a process whereby a bunch of images are being analyzed. For example, the system may be set up to temporarily store all images in an on-premise storage node. Then periodically (e.g., weekly), the system may be configured to analyze the images for storage elsewhere, depending on their sensitivity.

At operation304, the system analyzes the image to identify a sensitive portion of the image. The sensitive portion may include any text, object, or other information that is considered sensitive according to data compliance rules used by the system. For example, data compliance rules based on financial regulations may demand secure storage of personally identifiable information. Accordingly, the system may identify any such information (e.g., name, bank account number, etc.) in the text in an image and determine that the portion of the image containing that text is sensitive. Similarly, objects in the image may be considered sensitive. For example, medical scans may be considered sensitive, even if the scans themselves do not identify the patient.

As recognized by the present disclosure, however, not all of the image may be sensitive. For example, a scanned document may contain sensitive information in part of it (e.g., social security numbers, names, etc. in a header), while the rest of the document may contain non-sensitive information (e.g., publicly available information). As an illustrative example, the scanned document may be an attorney bar exam application form. The bar exam application form may be available on a state government's website, and as such most of the information in it may not be sensitive. However, the portions filled out by the applicant may include sensitive information.

Accordingly, the analysis at operation304can include identifying which portions of the image are sensitive, and which portions are not sensitive (e.g., contain no sensitive data). The analysis also includes identifying boundary information for the sensitive portions that define where in the original image the sensitive portions go. This boundary information can later be used to split the image (e.g., remove the sensitive portions) or put it back together.

At operation306, the processor may split the image into a sensitive portion and a non-sensitive portion. The sensitive portion may be those parts of the image that contain sensitive data, while the non-sensitive portions are those parts of the image that lack any sensitive data. This essentially involves splitting the image into two partial images. The image may be split using known techniques for splitting or splicing an image and using the results of the analysis from operation304.

At operation308, the processor generates merger metadata for the image. The merger metadata, as disclosed herein, includes both the boundary information (e.g., the information determined at operation304) and location data related to where the sensitive portion and the non-sensitive portion of the image will be stored.

After determining the merger metadata, the processor stores the sensitive portion of the image in a first distributed computing system (e.g., on-prem storage) at operation310and the non-sensitive portion of the image in a second distributed computing system (e.g., off-prem storage) at operation312. The processor also stores the merger metadata for the image at operation314, and the method300ends. The merger metadata may be stored in an image sensitivity database254, as discussed herein.

In some embodiments, the various operations may be performed in a different order, and/or two or more of the operations may be performed simultaneously. For example, the merger metadata may be generated (operation308) and stored (operation314) prior to splitting the image (operation306), in some embodiments. Similarly, operations310and312may be performed at the same time or sequentially.

In some embodiments, before identifying the sensitive portion of the image at operation314, the processor may determine whether the image has previously been split and stored across distributed computing systems. For example, the processor may generate a hash of the image and compare it to hashes stored in the image sensitivity database254. If there is a match, it may indicate that the image has already been split. Accordingly, instead of performing operations306-314for the image, the processor may simply create a link to the record in the image sensitivity database254that is associated with the image.

In some embodiments, the image may be split into more than two portions. For example, the system may have three or more tiers of storage, with each tier being associated with a different sensitivity level. In these embodiments, the sensitivity level of each part of an image may be determined, and the image may be split into one portion for each sensitivity level present. These portions may be sent to the corresponding storage tier. For example, a medical image may be split into three portions: a first portion that contains non-sensitive data, a portion that contains somewhat sensitive data (e.g., a patient's name), and a portion that contains highly sensitive data (e.g., a scan). The non-sensitive data may be sent to a public cloud storage cluster, while the somewhat sensitive data is sent to a more secure private cloud hosted by a third party, and the highly sensitive data is kept on-premise.

Referring now toFIG.4, illustrated is a flowchart of an example method400for responding to a request for an image that is stored across distributed computing systems, in accordance with embodiments of the present disclosure. The method400may be performed by hardware, firmware, software executing on a processor, or any combination thereof. For example, the method400may be performed by a processor (e.g., in a server in a hybrid cloud120). The method400begins at operation402, wherein the processor receives a request for an image.

The request may come from, for example, a user or an application running on a remote system. In some embodiments, the processor may authenticate that the requestor has permission to access, receive, or view the image. If the requestor is permitted to receive the image, at operation404, the processor determines that the image has been split into multiple portions. This may be done by looking the image up (e.g., by its name or other identifier) in an image sensitivity database254. The image sensitivity database254record for the image may indicate that the image has been split into one or more sensitive portions and one or more non-sensitive portions.

At operation406, the processor identifies the location of the sensitive and non-sensitive portions of the image. This may be done using information from the record for the image in the image sensitivity database254. For example, the location data may indicate where each portion is stored (e.g., the distributed computing system, server, etc. that has the portions of the image).

At operation408, the processor retrieves the sensitive and non-sensitive portions of the image from the distributed computing systems. For example, the processor may retrieve the sensitive portion of the image from an on-premise private cloud, while the non-sensitive portion of the image may be retrieved from an off-premise public cloud.

At operation410, the processor combines the sensitive and non-sensitive portions of the image. The processor may utilize boundary information stored in the image sensitivity database254to determine how to combine the sensitive and non-sensitive portions of the image. After combining the sensitive and non-sensitive portions of the image into a combined (e.g., complete) image, the processor returns the combined image to the requestor at operation412, and the method400ends.

Referring now toFIG.5, shown is a high-level block diagram of an example computer system501that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system501may comprise one or more CPUs502, a memory subsystem504, a terminal interface512, a storage interface516, an I/O (Input/Output) device interface514, and a network interface518, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus503, an I/O bus508, and an I/O bus interface unit510.

The computer system501may contain one or more general-purpose programmable central processing units (CPUs)502A,502B,502C, and502D, herein generically referred to as the CPU502. In some embodiments, the computer system501may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system501may alternatively be a single CPU system. Each CPU502may execute instructions stored in the memory subsystem504and may include one or more levels of on-board cache.

System memory504may include computer system readable media in the form of volatile memory, such as random access memory (RAM)522or cache memory524. Computer system501may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system526can be provided for reading from and writing to a non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive for reading from or writing to a removable, non-volatile optical disc such as a CD-ROM, DVD-ROM or other optical media can be provided. In addition, memory504can include flash memory, e.g., a flash memory stick drive or a flash drive. Memory devices can be connected to memory bus503by one or more data media interfaces. The memory504may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments.

One or more programs/utilities528, each having at least one set of program modules530may be stored in memory504. The programs/utilities528may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules530generally perform the functions or methodologies of various embodiments.

Although the memory bus503is shown inFIG.5as a single bus structure providing a direct communication path among the CPUs502, the memory subsystem504, and the I/O bus interface510, the memory bus503may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface510and the I/O bus508are shown as single respective units, the computer system501may, in some embodiments, contain multiple I/O bus interface units510, multiple I/O buses508, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus508from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses.

It is noted thatFIG.5is intended to depict the representative major components of an exemplary computer system501. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG.5, components other than or in addition to those shown inFIG.5may be present, and the number, type, and configuration of such components may vary. Furthermore, the modules are listed and described illustratively according to an embodiment and are not meant to indicate necessity of a particular module or exclusivity of other potential modules (or functions/purposes as applied to a specific module).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and image processing application96. The image processing application96may include instructions for performing various functions disclosed herein, such as splitting and/or recombining images based on the sensitivity of the image.

When different reference numbers comprise a common number followed by differing letters (e.g.,100a,100b,100c) or punctuation followed by differing numbers (e.g.,100-1,100-2, or100.1,100.2), use of the reference character only without the letter or following numbers (e.g.,100) may refer to the group of elements as a whole, any subset of the group, or an example specimen of the group.

In the foregoing, reference is made to various embodiments. It should be understood, however, that this disclosure is not limited to the specifically described embodiments. Instead, any combination of the described features and elements, whether related to different embodiments or not, is contemplated to implement and practice this disclosure. Many modifications, alterations, and variations may be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. Furthermore, although embodiments of this disclosure may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of this disclosure. Thus, the described aspects, features, embodiments, and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Additionally, it is intended that the following claim(s) be interpreted as covering all such alterations and modifications as fall within the true spirit and scope of the invention.