Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Drawings
The drawings filed on 10/24/2023 are accepted.
Specification
The specification filed on 10/24/2023 are accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 20 is neither proper independent claim nor proper dependent claim. Claim 10 calls for “a
Non-transitory machine readable medium” being dependent on method claims. It’s unclear whether the claim should be treated as independent claims as being directed towards “computer readable medium” or should be treated as dependent claims as being dependent on method claim 1. For examination purpose the claim is treated as dependent claims. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 - 4, 10 - 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al (herein after Shao) CN 113051590 B in view of Zhao et al (herein after Zhao) CN 110719158 A. (English translation attached)
Regarding claim 1, Shao teaches A method for federated learning, the method comprising (Shao Abstract) A data processing method and a related device, which are used for protecting private information of a client device during a federated learning process, and relate to the field of artificial intelligence).
identifying a first set of one or more devices in a plurality of devices as members of a master committee (Shao Page 32 Para 04) The server sends a second response message to the client device, where the. sub. second response message carries the secret share of each client device in U2 (i.e. master committee which could contain more than 80 client devices).
receiving, from a third set of one or more devices in the plurality of devices, a set of encrypted update values (Shao Page 35 Para 11) The transceiver module 501 is configured to receive the ciphertext .sub.cu sent by each .sub.of the N3 client devices (i.e. 3rd set of devices) to obtain N3 ciphertexts .sub.cu , the ciphertext .sub.cu includes a first part of the ciphertext .sub.c .sub.u0 and a second part The ciphertext c .sub.u1 and N .sub.3 are integers greater than or equal to 2, and each ciphertext c .sub.u in the N .sub.3 ciphertexts c .sub.u is encrypted by the corresponding client device among the N .sub.3 client devices using the encryption public key PK).
receiving, from a fourth set of one or more devices in the plurality of devices, decrypted aggregation results based on the encrypted aggregation results and cryptographic key shares of a private cryptographic key from the master committee (Shao Pages 27 and 23) the server runs the final decryption algorithm of the distributed threshold homomorphic encryption system on the received partial decrypted ciphertext to obtain the weighted average of the model parameters of N .sub.3 client devices……. Obtained by the random noise secret aggregation of each of the N .sub.2 secret shares, and the private key secret is the secret sharing of the first private key by each client device among the N .sub.4 client devices (i.e. fourth set of devices) One of the obtained multiple private key secrets, so the server can decrypt the second part of the ciphertext according to the preset final decryption. See Page 23 Para 12) the server runs the final decryption algorithm of the distributed threshold homomorphic encryption system on the received partial decrypted ciphertext to obtain the weighted average of the model parameters of N.sub.3 client devices (Server produces the encrypted aggregation results, then distributes the encrypted result back out to each device, and then each device reconstructs its own decryption material. Device then uses that reconstructed vector to partially decrypt producing a partially decrypted ciphertext. Device sends the partial decryption back which allows the server to combine the decryptions.)
and updating model parameters of the model based on the decrypted aggregation results (Shao Page 23 Para 12) the server runs the final decryption algorithm of the distributed threshold homomorphic encryption system on the received partial decrypted ciphertext to obtain the weighted average of the model parameters of N.sub.3 client devices.)
While Shao teaches federated machine learning methods to identify devices in a master committee and decrypting the devices based on their encrypted aggregation results, it does not explicitly disclose identifying a second set of one or more devices in the plurality of devices as members of a differential privacy (DP)-noise committee. However, Zhao in the same fields of endeavor teaches identifying a second set of one or more devices in the plurality of devices as members of a differential privacy (DP)-noise committee (Zhao Pages 9 and 10) we assume each participant before encrypting the data x of the noise r into the other, representing participant noise in plaintext in the t round communication. each client obtained from the TA public key PKP, he performs encryption algorithm is its private value client ui random generation value to generate a ciphertext……. Upon receiving the encrypted update, server S firstly running polymerization algorithm having Paillier homomorphic properties and obtained aggregation result).
receiving a set of encrypted noise values for differential privacy from the members of the DP-noise committee (Zhao pages 8-10) Common perturbations are gaussian distributions, Laplacian distributions, etc., and the present invention uses noise that is subject to a geometric distribution to generate perturbations that can be viewed as a discrete approximation of the Laplacian distribution, according to a discrete set used in the encryption scheme. We constructed DD-Private data perturbation by adding geometrically distributed noise to the raw data of each participant. Further teaches each participant before encrypting the data x of the noise r into the other, representing participant noise in plaintext in the t round communication. each client obtained from the TA public key PKP, he performs encryption algorithm is its private value client ui random generation value to generate a ciphertext……. Upon receiving the encrypted update, server S firstly running polymerization algorithm having Paillier homomorphic properties and obtained aggregation result)
aggregating the encrypted noise values and the encrypted update values to produce encrypted aggregation results (Zhao Pages 9 and 10) we assume each participant before encrypting the data x of the noise r into the other, representing the participant noise in plaintext in the t round communication. each client obtained from the TA public key PKP, he performs encryption algorithm is its private value client ui random generation value to generate a ciphertext……. Upon receiving the encrypted update, server S firstly running polymerization algorithm having Paillier homomorphic properties and obtained aggregation result. (Server’s homomorphic aggregation of ciphertexts produces an encrypted aggregation result of the encrypted noise and update values).
Therefore, it would have been obvious to one of ordinary skill in the art to incorporate Shao’s federated learning process of client device protection into Zhao’s teaching of privacy protection and learning accuracy using homomorphic cryptography. One would be motivated to do so to achieve differential privacy guarantees for federated learning updates while preserving the efficiency of Paillier-based secure aggregation. (Zhao Abstract)
Regarding Claim 2, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches, identifying the first set of devices comprises publishing a list of public keys of the members of the master committee to a bulletin board, wherein one or more of the plurality of devices are configured to access the bulletin board to verify the members of the master committee based on the published list of public keys. (Shao Page 31) the first client device may construct an Ethernet transaction, the first public key pk. sub. v0 is carried in the Ethernet transaction….. Then, each client device among the multiple client devices can write the respective first public key pk. sub. v0 to the respective Ethernet account….. Exemplarily, the first client device discloses the first public key of the first client device through the blockchain account of the first client device. In addition, the first client device determines the blockchain account (i.e. digital bulletin board) of the second client device, and queries the blockchain account of the second client device through the blockchain account of the first client device to obtain the second client device 's first public key……. The server sends a second response message to the client device, where the. sub. second response message carries the secret share of each client device in U2).
Regarding Claim 3, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches, the bulletin board is a blockchain. (Shao Page 30) In the embodiments of this application, the Ethernet network is a distributed network created based on the blockchain technology.)
Regarding Claim 4, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches, identifying the first set of devices comprises identifying a target size for the master committee, wherein the target size is computed based on a number of committee members required to reconstruct the private cryptographic key. (Shao Page 11) the first client device needs to share its private key secretly. Then, the first client device first sets the number T (i.e. Target Size) of secret sharing and the threshold value t (T>t), and then the private key is secretly shared to obtain T secret shares. Then, the first client device sends T secret shares to T client devices in the federated learning system 100, wherein each client device keeps one secret share, and different clients keep different secret shares. When data needs to be restored, any client device in the federated learning system 100 can restore the private key of the first client device through calculation as long as it obtains t shares of the T secret shares.)
Regarding Claim 10, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Zhao further teaches the received set of encrypted noise values includes a ciphertext of a plaintext message and the plaintext message comprises a round identifier. (Zhao Claim 5) the client end obtaining the public key PKP after executing the encryption algorithm to generate its private value the ciphertext wherein, representing participant noise in plain text in the t (i.e. round identifier) round communication, and rt (i) is only known to the user ui.)
Regarding Claim 11, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Zhao further teaches the received set of encrypted update values includes a ciphertext of a plaintext message and the plaintext message comprises a round identifier. (Zhao Claim 5) the client end obtaining the public key PKP after executing the encryption algorithm to generate its private value the ciphertext wherein, representing participant i noise in plain text in the t (i.e. round identifier) round communication, and rt (i) is only known to the user ui.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao with the additional features of Zhao. One would have been motivated to do so in order to prevent an adversary, including a malicious aggregator, from replaying a stale update ciphertext from an earlier round to violate clipping bound or corrupt the aggregation result. (Zhao Page 5)
Regarding Claim 12, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches publishing public keys of at least one of the committee members to a bulletin board. (Shao Page 35) The processing module is further configured to disclose the first public key of the first client device through the blockchain account of the first client device.)
Regarding Claim 13, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches the first set of devices are identified as members of the master committee for a first round; (Shao Page 32 Para 04) The server sends a second response message to the client device, where the. sub. second response message carries the secret share of each client device in U2
and the method further comprises: identifying a fifth set of one or more devices in the plurality of devices as members of the master committee for a second subsequent round; (Shao Page 5) The individual client devices include N 4 (i.e. the fifth set) The server receives the aggregate ciphertext from the client device)
providing model parameters for the model for a second round to each member of the master committee for the second round; (Shao Page 26) From this, it can be obtained that the values of the model parameters uploaded by the client devices in U are aggregated equal to the value m obtained after the encryption and decryption described above, i.e., the decryption result described above is correct.)
and causing the first set of devices to provide a set of state data to the fifth set of devices, wherein the fifth set of devices uses the set of state data (Shao Page 23) In the embodiment of the application, when the server receives the partial decryption ciphertext [Image Omitted] After that, decrypting the
ciphertext with the received part is of the number N 4 If N 4 And if the protocol is smaller than the threshold value t, ending the protocol. Otherwise, the server decrypts the received portion The text runs the final decryption algorithm.)
Regarding Claim 20, the combination of Shao and Zhao teaches all the limitations of claim 1 above, Shao further teaches A non-transitory machine readable medium containing program instructions that are executable by a set of one or more processors to perform the method of claim 1. (Shao Page 8) an embodiment of the present application provides a computer-readable storage medium, where a program is stored in the computer-readable storage medium, and the program enables a computer device to execute any one of the above-mentioned second or third aspects. method described.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Sato et al (herein after Sato) US 2020/0151715 A1.
Regarding claim 5, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach identifying the second set of devices as members of the DP-noise committee comprises identifying a target size for the DP-noise committee, wherein the target size is based on a ratio of known honest devices to total devices, however Sato in the same fields of endeavor teaches identifying the second set of devices as members of the DP-noise committee comprises identifying a target size for the DP-noise committee (Sato Para 0111) More specifically, known or well-known techniques may be employed as a consensus forming processing method. Specifically, it is conceivable to adopt, for example, an algorithm called Practical Byzantine Fault Tolerance (PBFT). PBFT is an algorithm in which consensus may be formed if consensus made among nodes more than or equal to a constant ratio (two-thirds or more) between all nodes participating in consensus forming (i.e., verification nodes).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Sato. One would have been motivated to do so to decide how large to make the committee based on the fraction of devices expected to be dishonest. Rather than guessing at a committee size, the system could rely on two-thirds threshold to ensure the committee still functions correctly even if some fractions of its members are malicious. (Sato Para 0016).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Baek et al (herein after Baek) WO 2021/230449 A1.
Regarding claim 6, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach the set of encrypted noise values from a given member of the DP-noise committee are Gaussian noise data generated independently from any other member of the DP-noise committee, however Baek in the same fields of endeavor teaches the set of encrypted noise values from a given member of the DP-noise committee are Gaussian noise data generated independently from any other member of the DP-noise committee (Baek Page 25) As another example, when the processor 13 executes the instructions included in the differential information protection data generation module 17d, the electronic device 10 may be private in a differential privacy manner so that the private data is not specified. Differential information protection data (e.g., Gaussian noise) that is data for encrypting data may be generated.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Baek. One would have been motivated to do so since Gaussian noise was already a well-established and widely adopted choice for differential privacy and applying each committee member’s own noise-generation process using this Gaussian technique is no more than the combination of known elements (distributed committee-based DP-noise generation and Gaussian noise as the DP mechanism) (Baek Page 3)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Sun et al (herein after Sun) US 2021/0089882 A1.
Regarding claim 7, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach the set of encrypted noise values from a given member of the DP-noise committee comprise an additive share of a noise budget, however Baek in the same fields of endeavor teaches the set of encrypted noise values from a given member of the DP-noise committee comprise an additive share of a noise budget (Sun Para 0037) A general method for enforcing a query function ƒ with the (∈, δ)-differential privacy is to apply additive noise calibrated to the sensitivity of ƒ. A general method for ensuring a deterministic query ƒ to be the (∈, δ)-differential privacy is via perturbation mechanisms that add calibrated noise to the query's output.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Baek. One would have been motivated to do so to improve the system by giving it a principled, mathematically grounded way to decide how much noise to add rather than each committee member contributing an arbitrary noise share. The noise share is calibrated to the sensitivity of the underlying query (the model update), ensuring the DP guarantee is met rather than approximated. (Sun Para 0006).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Rao et al (herein after Rao US 2019/036883 A1.
Regarding claim 8, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach each particular device in the third set of devices randomly selects itself to contribute updates in a given round using a pseudorandom generator seeded with a publicly verifiable random value and a public key of the particular device, however Rao in the same fields of endeavor teaches each particular device in the third set of devices randomly selects itself to contribute updates in a given round using a pseudorandom generator seeded with a publicly verifiable random value and a public key of the particular device (Rao Para 0055) FIG. 3 illustrates an example of a program 300 that may decrypt a content service's ciphertext and perform an encryption operation under the public key of a user device pk.sub.d consistent with certain embodiments of the present disclosure. As illustrated, the program may use as constants a sender's secret key, the receiver's public key, and/or a pseudorandom seed value.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Baek. One would have been motivated to do so to improve the system by giving each device a way to determine its own participation status locally and verifiably, without needing to communicate with a central server or other devices to find out if it’s selected. The output depends on the device’s own key material combined with a shared public random seed. (Rao Abstract)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Yuan SG 10202102327U A.
Regarding claim 9, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach publishing a clipping bound to a bulletin board, wherein the received set of encrypted update values comprises are locally generated at each of the third set of devices and are clipped by the clipping bound, however Rao in the same fields of endeavor teaches publishing a clipping bound to a bulletin board, wherein the received set of encrypted update values comprises are locally generated at each of the third set of devices and are clipped by the clipping bound (Yuan Para 0006) In another aspect, a device for verifying data integrity includes: one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to: obtain a data content, an encrypted data clip, a signature, and an encrypted hash recorded on a blockchain).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Yuan. One would have been motivated to do so in order to address the concern of the bulletin board publishing system parameters for device-side verification. Applying clipped data value together with a cryptographic integrity mechanism on an immutable blockchain would yield a publicly verifiable, tamper-resistant clipping bound that devices can trust and rely on when generating their own bounded updates, with no unexpected results. (Yuan Para 0002-0004).
Claims 14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and in further view of Chalkias et al (herein after Chalkias) US 2021/0336789 A1.
Regarding claim 14, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach the encrypted noise values and the encrypted update values comprise a plurality of ciphertexts, wherein aggregating the encrypted noise values and the encrypted update values comprises generating a summation tree for each of the plurality of ciphertexts, however Chalkias in the same fields of endeavor teaches the encrypted noise values and the encrypted update values comprise a plurality of ciphertexts, wherein aggregating the encrypted noise values and the encrypted update values comprises generating a summation tree for each of the plurality of ciphertexts (Chalkias Para 0073) ….. summation trees, each internal node contains a numeric value which equals the sum of its children amounts. Thus, all of the leaf balances are filled up in a bottom-up order, such that the final balance of the root node is the summation of all leaf node numeric values.
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Chalkias. One would have been motivated to do so in order to enable participants in a multi-party protocol to verify that their individually contributed values were completely included in a publicly-reported aggregation total. Supplementing homomorphic-summation approach with Chalkias summation-tree structure, in which each internal node’s value is verifiably the sum of its children’s values up to a root reflecting total aggregation. (Chalkias Abstract)
Regarding claim 16, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach each summation tree comprises a set of leaf and non-leaf nodes; and each of at least a subset of the plurality of devices verifies the updating of the model parameters by downloading a set of one or more of the summation trees and verifying at least a subset of the set of leaf and non-leaf nodes of each summation tree, however Chalkias in the same fields of endeavor teaches each summation tree comprises a set of leaf and non-leaf nodes; and each of at least a subset of the plurality of devices verifies the updating of the model parameters by downloading a set of one or more of the summation trees and verifying at least a subset of the set of leaf and non-leaf nodes of each summation tree. (Chalkias Para 0195) the authentication path generator 908 can recursively identify every node from the user's leaf node back to the root node of the sparse-tree. The authentication path generator 908 can provide this list of nodes as the user's authentication path.) (user downloads their path and verifies)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Chalkias. One would have been motivated to do so in order to identify every node from a user’s leaf node back to the root and providing that list of nodes for the user to download and verify, enabling an individual participant to independently confirm that their own contribution is correctly reflected in a reported aggregate total without requiring that participant to download or inspect the entire tree structure. (Chalkias Para 0006)
Regarding claim 17, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach verifying leaf nodes comprises confirming that ciphertexts are committed to and confirming that zero-knowledge (ZK)-proofs are valid, however Chalkias in the same fields of endeavor teaches verifying leaf nodes comprises confirming that ciphertexts are committed to and confirming that zero-knowledge (ZK)-proofs are valid. (Chalkias Para 0026) the cryptographic proof of liabilities system can generate a zero-knowledge range proof for every internal node of the sparse-tree leading up the root node that demonstrates that the committed liability of each node is a small positive number within a predetermined range of numbers. Thus, the cryptographic proof of liabilities system can show at every level of the sparse-tree that the liabilities represented therein are expected.
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Chalkias. One would have been motivated to do so in order to generate a zero-knowledge range proof for each node in the tree to demonstrate that the committed value at that node is valid, small positive number within an expected range. Applying ZK proof technique to achieve tamper-detection and validity-assurance benefit would have no unexpected results as it is well known in the art. (Chalkias Para 0008)
Regarding claim 18, while Shao in view of Zhao disclose the limitations of claim 1, the combination fails to explicitly teach verifying non-leaf nodes comprises confirming that the non-leaf node equals a sum of its child nodes, however Chalkias in the same fields of endeavor teaches verifying non-leaf nodes comprises confirming that the non-leaf node equals a sum of its child nodes. (Chalkias Para 0169) Consider a prover A who creates a summation tree with negative balances, negative partial sums, or incorrect partial sums. We call a node corrupted if the value assigned to it is negative or its value is not the sum of values for its two children (only for non-leaf nodes).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao and Zhao with the additional features of Chalkias. One would have been motivated to do so in order to ensure that node’s value equals the sum of its two children’s values in order to detect a corrupted node. With Shao’s summation-tree aggregation structure, both references solve the problem of ensuring that a potential dishonest aggregator has not tampered with immediate aggregation values within a hierarchical summation tree. (Chalkias Para 002)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao and Chalkias and in further view of Axenie et al (herein after Axenie) WO 2020/043267 A1.
Regarding claim 15, while Shao in view of Zhao and Chalkias disclose the limitations of claims 1 and 14, the combination fails to explicitly teach publishing vertices of the summation trees on a bulletin board, wherein at least one device of the third set of devices can verify that update values from the at least one device were included in the model parameter update, however Axenie in the same fields of endeavor teaches publishing vertices of the summation trees on a bulletin board, wherein at least one device of the third set of devices can verify that update values from the at least one device were included in the model parameter update (Axenie Page 13) Finally, a score 111 is calculated depending on the model parameters (e.g. a squared log function, size and level of the tree, learning model for scoring panel). The score 111 is a numeric value characterizing an anomaly (e.g. high normalized value close to 1) or normal data (e.g. low value, close to 0) …. such that the incoming data is encoded in a histogram update (i.e. counting / summation) at each tree leaf level).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao, Zhao and Chalkias with the additional features of Axenie. One would have been motivated to do so in order to give the system an efficient way to encode and combine many devices’ individual contributions into a single hierarchical structure rather than aggregating all contributions in one flat operation. (Axenie Page 2)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Shao in view of Zhao, Axenie, Chalkias and in further view of Liu et al (herein after Liu) CN 114239062 A1.
Regarding claim 19, while Shao in view of Zhao, Axenie, and Chalkias disclose the limitations of claims 1,14, 15, and 18, the combination fails to explicitly teach each child of the non-leaf node is a polynomial, wherein confirming that the non-leaf node equals the sum of its child nodes comprises performing polynomial identity testing on the child nodes, however Liu in the same fields of endeavor teaches each child of the non-leaf node is a polynomial, wherein confirming that the non-leaf node equals the sum of its child nodes comprises performing polynomial identity testing on the child nodes. (Liu Page 8) for the parameter node x, if it is non-leaf node, the data owner generates a random polynomial qx, the polynomial satisfies: a) the random polynomial times equal to the child node number of corresponding non-leaf node minus 1, b) (0 represents the variable x value in the polynomial is 0).
Therefore, it would have been obvious to one of ordinary skill in the art to modify the disclosure of Shao, Zhao, Chalkias, Axenie with the additional features of Liu. One would have been motivated to do so in order to efficiently verify a relationship between a parent node and its children within a hierarchical tree structure without requiring a verifier to reconstruct or directly compare the full underlying values. Instead of downloading and directly comparing potentially large ciphertext or polynomial values at every level, a verifying device only needs to evaluate the relevant polynomials at a single point and check that the evaluations match. This reduces the computational and communication cost of verification, which matters at scale in a system with many devices verifying many nodes across many summation trees. (Liu Page 2)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDUL HAFEEZ MIRZA whose telephone number is (571)270-0784. The examiner can normally be reached Monday - Friday, 7:30 a.m. - 5 p.m. ET..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shewaye Gelagay can be reached at (571) 272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.H.M./Examiner, Art Unit 2436
/MOEEN KHAN/Primary Examiner, Art Unit 2436
Prosecution Insights