Abstract:
A proximity-based data security method comprises identifying, by a data-owner device, at least N proximally-located devices; verifying, by the data-owner device, the at least N proximally-located devices as at least N trusted devices; encrypting a data set; splitting the encrypted data set into at least N data subsets; transmitting the at least N data subsets to the at least N trusted devices; digitally signing, at each of the at least N trusted devices, the received encrypted data subset and generating a digital signature; and storing the digital signature and the received encrypted data subset at each of the at least N trusted devices.

Description:
RELATED APPLICATION 
       [0001]    This patent application claims the benefit of U.S. Provisional Patent Application No. 62/308,211 filed on Mar. 14, 2016. 
     
    
     FIELD 
       [0002]    The present disclosure relates to data and information security and particularly to a system and method for proximity-based collaborative information security. 
       BACKGROUND 
       [0003]    The past few decades have witnessed information explosion in human history. The advent of Internet of Things (IoT) and connected devices further contribute to the data deluge: data are being generated at an accelerated pace. More and more companies have started relying on “big data” to extract value and improve business performance. With customer data increasingly accessible online and frequent reports of data breaches, customers are more concerned about protecting their privacy and personal data than ever before. 
         [0004]    Despite advances in cryptography, data or information security remains a big challenge in modern computing and people&#39;s daily lives. Data breaches and theft that happened to large corporations made national or international headlines dozens of times in the past couple of years, not to mention countless hacks and computer intrusions that are happening every day towards ordinary consumers. Even encrypted data is not bullet-proof and can be eventually breached in a finite amount of time with sufficient computing power. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0005]      FIG. 1  is a simplified diagram illustrating an exemplary Internet of Things network; 
           [0006]      FIG. 2  is a simplified diagram illustrating an exemplary embodiment of proximity-based collaborative information security according to the teachings of the present disclosure; 
           [0007]      FIG. 3  is a simplified flowchart illustrating an exemplary process to encrypt data according to the teachings of the present disclosure; and 
           [0008]      FIG. 4  is a simplified flowchart illustrating an exemplary process to decrypt data according to the teachings of the present disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0009]      FIG. 1  is a simplified diagram illustrating an exemplary Internet of Things (IoT) network  10  as one environment in which proximity-based collaborative information security system and method may operate. Conceptually, an IoT network consists of three main types of interconnected components, (a) IoT nodes  12 , (b) fog nodes  14 , and (c) clouds  16 , interconnected via the Internet (global computer networks)  17 . The IoT nodes  12  are devices equipped with various sensors and generate local data. The fog nodes  14  are networks of IoT devices that are connected to one another through short range communications such as Wi-Fi Direct, ZigBee, Bluetooth, etc. The clouds  16  are application servers at data centers that provide internet services and where customer data reside. In many cases, IoT nodes  12  can be part of a fog network, and fog nodes  14  may or may not connect to an internet cloud  16  for fog computing. In a fog network in which many end-user clients or near-user edge devices collaborate to carry out storage, communication, control, configuration, measurement and management functions, data security can also be improved via collaboration of proximal devices. The present disclosure addresses data security from the perspective of proximity-based collaboration: instead of storing encrypted data in one location, the encrypted data owned by a data-owner device  18  is partitioned into multiple data subsets, which are in turn distributed and stored across multiple trusted devices  20  that are physically separate from one another, as shown in  FIG. 2 . 
         [0010]      FIG. 3  is a simplified flowchart illustrating an exemplary process to encrypt data for secure data storage according to the teachings of the present disclosure. The data set to be protected may initially reside on one device (the data-owner device  18 ), which is located proximally to a plurality of other devices  20 , with whom the data-owner device  18  maintains a pre-established trusted relationship. The data-owner device  18 , which possesses the data set, prepares the original set, as shown in block  22 . The data-owner device  18  then identifies and verifies the identities of those trusted devices  20  located in its proximity, as referenced by numeral  24 . Proximity is defined as having a distance close enough to achieve a form of wireless and wired communication (e.g., WiFI or WiFi Direct, Bluetooth, NFC, ZigBee, ZigBee RF4CE, IrDA, ANT, ANT+, Nike+, or any suitable protocol now known or to be developed) relied upon for device-to-device communication. The data-owner device  18  then encrypts the data set, and divides the encrypted data set into a plurality of data subsets, with each data subset represented by a sequence number to denote its proper order in the entire data set, as shown in blocks  26  and  28 . These encrypted data subsets are then transmitted to the plurality of trusted devices  20  that are proximal to the data-owner device  18 , as shown referenced by numeral  30 . 
         [0011]    Each trusted device  20  that receives its respective encrypted data subset then digitally signs the data subset now in its possession, and packages it with its meta data (i.e., data subset sequence number, the public key or the digital certificate of the person who signed the encrypted data subset, and the digest of the data subset), and stores the packaged data subset locally, as shown in blocks  32 - 38 . The original data set is now split into N packages and resides in N trusted devices, where N is the number of devices that jointly hold all the encrypted data subsets. 
         [0012]    It should be noted that the data-owner device  18  may optionally retain one of the encrypted data subsets itself, as shown in block  40 . If that is the case, the data set should be divided into N+1 subsets, rather than just N subsets. There are multiple methods to partition a data set. One method is to simply divide the data set sequentially into N+1 subsets of various sizes. Another method is to divide the data set into blocks of fixed number of bytes (e.g. blocks of 4 bytes) and assign these data blocks to N+1 devices in a round-robin fashion until all the data blocks are assigned. Finally, the data-owner device  18  records the identities of the trusted devices that retain the data subsets paired with the associated sequence numbers of the data subsets, as shown in block  42 . 
         [0013]    In the reverse direction, illustrated in  FIG. 4 , the data-owner device  18  may wish to recover and reconstitute the protected data. The data-owner device  18  may detect that all of the trusted devices  20  that possess a data subset are located nearby, and can achieve wireless or wired communication with all of them, as shown in block  50 . The data-owner device  18  and the trusted devices  20  then authenticate one another device&#39;s identity, as shown in block  52 . Each of the trusted devices  20  is then requested by the data-owner device  18  to transmit the data package it possesses to the data-owner device  18 , as shown in block  54 . The data package from each trusted device  20  contains the encrypted data subset and the meta data, which include its sequence number, the digest for the encrypted data subset, and the public key or the digital certificate of the person who signed the encrypted data subset during the encryption flow ( FIG. 3 ). The data-owner device  18  then executes a signature verifying algorithm that uses the public key of a trusted device to verify its digital signature for each trusted device that holds a data subset, as shown in block  56 . The data-owner device  18  then calculates the digest for each encrypted data subset it receives from a trusted device and compares it against the digest stored in the meta data, as shown in block  58 . If there is a match for all the encrypted data subsets, the data-owner device  18  merges the data subsets according to their sequence numbers, and decrypt the merged data set, as shown in blocks  60  and  62 . Thus in this manner the original data set is reconstituted back at the data-owner device  18 . 
         [0014]    Because the breach of all but one subset of data will not result in the compromise of the entire original data set, a malicious entity must obtain ALL the data stored in multiple trusted devices  20  in order to obtain the entire data set, which is a much more difficult task. For added security, access to data stored in other devices can only happen when all the devices are in close proximity of one another. 
         [0015]    Accordingly, a cryptographic application can be developed to perform the data split, merger, distribution, and digital signing operations, in addition to the conventional encryption and decryption operations. This application is considered a proximity-based collaborative software because it requires all the involved devices to be physically close to one another in a fog network and collaborate in order for this approach to work. 
         [0016]    It should be noted that not just the data-owner device  18 , but any one of the trusted devices  20  that possess a data subset can recall all the data packages to merge and decrypt the data set as long as all the necessary authentications with involved parties can be successfully performed. 
         [0017]    It should also be noted that the proximity-based collaborative information security system and method described herein can work with any encryption and authentication (digital signature) algorithms now known or to be developed, including Advanced Encryption Suite (AES), Rivest-Shamir-Adleman (RSA), etc., and that it may be implemented in many different scenarios and is not limited to IoT applications and fog networks. 
         [0018]    This proximity-based collaborative information security idea can be extended to secure data transmission against eavesdropping. In order to transmit a set of data to a remote site, the entire data set can be first encrypted and split into multiple pieces, which are then transmitted to the destination over multiple, and possibly physically separated, communication channels. The transmitted data are merged back together at the receiving end before decryption. Eavesdropping of all but one communication channel will not result in the compromise of transmitted data. 
         [0019]    Another area where this idea can be applied is proximity-based authentication, in which authentication can take place only when the authenticating devices are in close proximity with the devices to be authenticated. An example of the applicable domains is resource (e.g. systems, building, device) access. 
         [0020]    Requiring all the trusted devices to be in close proximity of the data-owner device  18  to recover the protected data could limit data availability to certain extent. To achieve a balance between data availability and data confidentiality, the idea can be further generalized by introducing a level of redundancy to an original data set such that it can be partitioned among N nodes (N≧2) with at least P nodes (P is an integer between 2 and N) present, of which at least Q nodes (Q is an integer between 2 and P) must be in close proximity of one another, in order to recover the original data set. The N nodes can be a combination of clouds, IoT nodes, and devices in fog networks. The P nodes represent the minimal quorum needed to recover the original data set when a subset of them (Q nodes) are physically close to one another. The physical barrier among these Q nodes is what makes the information security mechanism more enhanced over the traditional approach. 
         [0021]    An even stronger scheme would be to partition not only the data set but also the keys used for encryption and/or decryption among N nodes. 
         [0022]    The inventive concepts described herein can be used in the following application domains: 
         [0023]    Collaborative fog computing: a task cannot be performed unless all the parties each holding a partial data set are present in a fog network. 
         [0024]    Secure data storage: data is encrypted and stored across multiple storage devices such as a smartphone, a PC, and a watch, and decryption can take place only when all these devices are in close proximity of one another. 
         [0025]    Secure data transmission: data is first encrypted and split into multiple portions, which are then transmitted over multiple communication channels, and merged before decryption. 
         [0026]    Data integrity check and validation: decryption operation is performed against a data set that is merged from encrypted data sets stored on multiple devices. 
         [0027]    Order delivery and mobile payment: when an order is placed online, a confirmation code is sent to a user&#39;s mobile device. When the order is actually delivered, the delivery person must obtain and verify the confirmation from the device the user specified earlier to make sure the order is delivered to the right person at the right place. 
         [0028]    Authentication: authentication can take place only when the authenticating devices are in close proximity with the devices to be authenticated. 
         [0029]    Authorization: encrypted data sets are stored on multiple devices. Authorization can be achieved by giving access to a data set that a user controls. 
         [0030]    Resource access (e.g., badge, garage, lock) and object and data matching (e.g., label, parked car finder, image matching): data sets to be matched are encrypted, digitally signed, and physically separated. When ready to match data sets, physically separated devices must be close to one another and merged data must be decrypted. If decryption fails, no match is found and no access shall be given. 
         [0031]    The features of the present invention which are believed to be novel are set forth below with particularity in the appended claims. However, modifications, variations, and changes to the exemplary embodiments described above will be apparent to those skilled in the art, and the proximity-based collaborative information security system and method described herein thus encompasses such modifications, variations, and changes and are not limited to the specific embodiments described herein.