WO2021227241A1 - 智能电网中抗密钥泄露的加密数据聚合的统计分析方法 - Google Patents
智能电网中抗密钥泄露的加密数据聚合的统计分析方法 Download PDFInfo
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Definitions
- the invention belongs to the field of smart grid big data analysis and information security assurance, and particularly relates to a statistical analysis method of encrypted data aggregation resistant to key leakage in the smart grid.
- the smart grid has introduced some emerging information technologies on the basis of the traditional power grid system, such as cloud computing, artificial intelligence, cryptography, etc., and established an advanced energy measurement infrastructure (Advanced Metering Infrastructure, AMI) on top of the traditional power transmission network. ), used for two-way transmission of power consumption data of grid users and control instructions and electricity price information fed back by the smart grid control center.
- AMI Advanced Metering Infrastructure
- the core of AMI is an embedded device called a smart meter, which is installed in the homes of grid users to periodically collect electrical energy data and report it to the control center.
- the control center uses the fine-grained user power data collected by the meter.
- Various data analysis techniques dig out useful information, monitor the operation of the system according to the analysis results, and then dynamically adjust and optimize the power generation and distribution plan of the grid system.
- the control center can also send some feedback control instructions, electricity price information, and power outage information to the smart meter through the data channel.
- the grid user can learn this information by interacting with the smart meter, and then
- the homomorphic encryption algorithm is widely used in the design of the algorithm. Due to the characteristics of the homomorphic encryption algorithm to maintain addition or multiplication, after the data is encrypted, it can be efficiently aggregated, and the control center can use the decryption private key Directly decrypt the aggregated ciphertext to obtain some statistical values without decrypting the ciphertext of a single user, thereby effectively protecting user privacy and data confidentiality.
- Lu et al. proposed a smart grid-oriented data aggregation protocol using the Paillier homomorphic encryption algorithm.
- a super-increasing sequence was combined with an encryption algorithm to achieve the aggregation of multiple types of data.
- the aggregation gateway node needs to perform multiple time-consuming bilinear mapping operations, which leads to a large computational cost of the protocol, which is not suitable for some systems that require high real-time performance.
- Chen et al. used the Boneh-Goh-Nissim (BGN) homomorphic encryption algorithm to propose a privacy-protected multifunctional data aggregation scheme. They used the multiplicative homomorphism of BGN to enable the control center to obtain data by decrypting the aggregated ciphertext. The sum and variance values of.
- the purpose of the present invention is to overcome the shortcomings of the prior art and provide a statistical analysis method of encrypted data aggregation that is resistant to key leakage in the smart grid.
- a statistical analysis method of encrypted data aggregation resistant to key leakage in a smart grid including:
- the trusted center generates the security parameters involved in the method, and distributes the public and private keys of each communication entity, which includes smart meters, fog nodes, cloud servers, and control centers; then the trusted center issues security parameters The public parameters in, and send each private key to the corresponding communication entity through a secure channel;
- the smart meter encrypts the collected user energy consumption data to obtain the meter data ciphertext, signs the meter data ciphertext to obtain the meter data signature, and sends the meter data ciphertext and the meter data signature as the electricity meter data to the corresponding Of fog nodes to aggregate;
- Fog-level aggregation After the fog node receives all the electricity meter data sent by the smart meter in the corresponding user area within the preset period, it verifies the electricity meter data signature in the electricity meter data. If the verification passes, the fog node performs the verification on the electricity meter data.
- the fog-level aggregated ciphertext is obtained by aggregation, and the aggregated value is signed to obtain the fog-level ciphertext signature.
- the fog node sends the fog-level aggregated ciphertext and the fog-level ciphertext signature as aggregated data to the cloud server for storage;
- the control center sends a challenge message to the cloud server.
- the challenge information includes a list of user areas for data analysis and a random matching coefficient sequence.
- the cloud server uses each user area in the user area list stored in it.
- the aggregated data and random matching coefficient sequence generated corresponding verifiable response information of encrypted aggregated data, and sent to the control center;
- the control center verifies the response information returned by the cloud server to determine the integrity of the encrypted aggregated data. If the verification is passed, the control center decrypts the encrypted aggregated data to obtain the average of the energy consumption data of all challenged users Value and variance.
- the security parameters in step S1 include the security parameters of the homomorphic encryption algorithm against key leakage and the security parameters of the linear homomorphic digital signature algorithm.
- the smart meter combines random blinding technology to encrypt user power consumption data using a homomorphic encryption algorithm against key leakage, and decrypts the response information using a homomorphic decryption algorithm against key leakage in step S5 .
- step S3 the fog node verifies the electricity meter data signature by a batch verification method.
- step S1 includes:
- the trusted center sets an elliptic curve E on the finite field F p , and determines a bilinear mapping based on the elliptic curve E G 1 ⁇ G 1 ⁇ G 2 , where p is a large prime number, G 1 is an additive cyclic group of order q, G 2 is a cyclic group of q factorial; the trusted center selects the generator P of the additive cyclic group G 1 , Set the number of fog nodes in the smart grid system to N, and the number of smart meters in each user area is
- the Trusted Center sets up two anti-collision and safe hash functions H 1 : ⁇ 0,1 ⁇ * ⁇ G 1 , Among them, ⁇ 0,1 ⁇ * is a bit string of arbitrary length, Is a cyclic group of residue multiplications that is relatively prime to q;
- the signature verification public key Y i y i P corresponding to the key y i ⁇ Z q;
- the trusted center sends the private key p 1 to the control center through the secure channel, and sends the private key y ij , the secret parameter ⁇ ij and the secret parameter s ij to the smart meter SM ij , and the secret parameter ⁇ i and the secret parameter s i And the private key y i are sent to the fog node FN i .
- step S2 includes:
- the smart meter SM ij selects a random number r ij ⁇ Z n to calculate the cipher text of the meter data Among them, m ij ⁇ [0,MAX] is user electric energy consumption data, MAX is the maximum value of consumption data, and MAX is less than p 2 ;
- the smart meter SM ij obtains the current time stamp t ij and calculates the meter data signature
- Smart meter SM ij integrates meter data Send to the corresponding fog node FN i .
- step S3 includes:
- the fog node FN i receives all the smart meters SM ij in the corresponding user area within the preset period, Meter data sent Then, verify the meter data signature ⁇ ij sent by all smart meters SM ij through the following equation:
- step S32 If the verification in step S31 is passed, the fog node FN i generates the first intermediate state aggregation value And the second intermediate state aggregation value
- the fog node FN i calculates fog-level aggregated ciphertext, where the fog-level aggregated ciphertext includes the first fog-level aggregated ciphertext And the second fog level aggregated ciphertext
- the fog node FN i sends the aggregated data ⁇ CT i , SCT i , ⁇ i ⁇ to the cloud server for storage.
- step S4 includes:
- the control center generates challenge information ⁇ L, chal ⁇ and sends it to the cloud server, where L is the user area list, chal is a random matching coefficient sequence of length ⁇ ,
- the cloud server computes the cloud-level aggregated ciphertext, where the cloud-level aggregated ciphertext includes a first aggregated ciphertext component Second aggregate ciphertext component And the third aggregate ciphertext component
- the cloud server calculates the random number based on the random number ⁇ , the random number ⁇ and the cloud-level aggregated ciphertext And random numbers And the signature of the fog-level aggregated ciphertext of the user area in the user area list L Generate the corresponding aggregate signature
- step S5 includes:
- the control center calculates the random number based on the random number ⁇ , the random number ⁇ and the cloud-level aggregated ciphertext And random numbers And calculate the aggregate value of the random matching coefficient And verify the validity of the signature through the following equation:
- step S52 If the signature verification in step S61 is passed, use the anti-key leakage homomorphic decryption algorithm to calculate the true number At the bottom is The discrete logarithm under and divide the result by Obtain the sum M of the power consumption data of all users in each user area in the user area list, namely
- the control center uses the private key p 1 to calculate discrete logarithms separately with in Is a bilinear pair, and then calculate the sum of squares of the original data of the smart meter in each user area of the challenge
- the control center calculates the average value:
- the control center calculates the variance:
- the present invention uses a homomorphic encryption algorithm that is resistant to key leakage to encrypt data.
- the smart meter uses the public key of the control center to generate ciphertext using random blinding technology, even if the private key of the control center is in some special In case of leakage, it will not cause a single ciphertext to be decrypted in the entire smart grid system, which can effectively protect user privacy and data confidentiality;
- This party has designed a lightweight batch data integrity verification technology, so that the control center can verify the integrity of all encrypted aggregated data requested within a constant time complexity, which is in accordance with the number of areas requested.
- the number of smart meters in the area is irrelevant, which can effectively ensure the integrity of the encrypted data of the entire smart grid system;
- This party can provide the control center with flexible data statistical analysis and query capabilities.
- the control center or service provider can selectively specify the user area of interest, that is, can specify an arbitrary user area index to the cloud server It is used for statistical analysis; at the same time, the cloud server can provide enough information to the control center without destroying the privacy of any single user, so that the control center can use this information to calculate the sum of all users' electricity consumption data in the designated user area. , Mean and variance;
- the control center can quickly verify the integrity of encrypted aggregated data with a constant amount of calculation, thereby performing statistical analysis of encrypted data in real time;
- the statistical analysis method of encrypted data aggregation for anti-key leakage in the smart grid of the present invention is based on the fog computing architecture, and uses fog node servers and cloud servers deployed at the network boundary to relieve the computing and storage pressure of the business system;
- Figure 1 is a schematic diagram of a smart grid system
- Figure 2 is a flow chart of the present invention.
- the present invention provides a statistical analysis method of encrypted data aggregation resistant to key leakage in smart grid:
- the statistical analysis method of encrypted data aggregation against key leakage in smart grid includes:
- the trusted center generates the security parameters involved in the method, and distributes the public and private keys of each communication entity, which includes smart meters, fog nodes, cloud servers, and control centers; then the trusted center issues security parameters And send the private keys to the corresponding communication entities through the secure channel.
- the security parameters in step S1 include the security parameters of the homomorphic encryption algorithm against key leakage and the security parameters of the linear homomorphic digital signature algorithm.
- the step S1 includes:
- the trusted center sets an elliptic curve E on the finite field F p , and determines a bilinear mapping based on the elliptic curve E G 1 ⁇ G 1 ⁇ G 2 , where p is a large prime number, G 1 is an additive cyclic group of order q, G 2 is a cyclic group of q factorial; the trusted center selects the generator P of the additive cyclic group G 1 , Set the number of fog nodes in the smart grid system to N, and the number of smart meters in each user area is
- the Trusted Center sets up two anti-collision and safe hash functions H 1 : ⁇ 0,1 ⁇ * ⁇ G 1 , Among them, ⁇ 0,1 ⁇ * is a bit string of arbitrary length, Is a cyclic group of residual multiplications that is relatively prime to q.
- the signature verification public key Y i y i P corresponding to the key y i ⁇ Z q.
- the trusted center sends the private key p 1 to the control center (CC) through the secure channel, and sends the private key y ij , the secret parameter ⁇ ij and the secret parameter s ij to the smart meter SM ij , and the secret parameter ⁇ i , secret
- the parameter s i and the private key y i are sent to the fog node FN i .
- the smart meter encrypts the collected user energy consumption data to obtain the meter data ciphertext, signs the meter data ciphertext to obtain the meter data signature, and sends the meter data ciphertext and the meter data signature as the electricity meter data to the corresponding Of fog nodes are aggregated.
- step S2 the smart meter combines random blinding technology to encrypt user power consumption data using a homomorphic encryption algorithm against key leakage, and in step S5, a homomorphic decryption algorithm against key leakage is used to respond Information is decrypted.
- the step S2 includes:
- the smart meter SM ij selects a random number r ij ⁇ Z n to calculate the ciphertext of the meter data Among them, m ij ⁇ [0,MAX] is user electric energy consumption data, MAX is the maximum value of consumption data, and MAX is less than p 2 .
- the smart meter SM ij obtains the current time stamp t ij and calculates the meter data signature
- Smart meter SM ij integrates meter data Send to the corresponding fog node FN i .
- Fog-level aggregation After the fog node receives all the electricity meter data sent by the smart meter in the corresponding user area within the preset period, it verifies the electricity meter data signature in the electricity meter data. If the verification passes, the fog node performs the verification on the electricity meter data.
- the fog-level aggregated ciphertext is obtained by aggregation, and the aggregated value is signed to obtain the fog-level ciphertext signature.
- the fog node sends the fog-level aggregated ciphertext and the fog-level ciphertext signature as aggregated data to the cloud server for storage.
- the step S3 includes:
- the fog node FN i (the fog node server) receives all the smart meters SM ij in the corresponding user area within the preset period, Meter data sent Then, verify the meter data signature ⁇ ij sent by all smart meters SM ij through the following equation:
- step S32 If the verification in step S31 is passed, the fog node FN i generates the first intermediate state aggregation value And the second intermediate state aggregation value
- the fog node FN i calculates fog-level aggregated ciphertext, where the fog-level aggregated ciphertext includes the first fog-level aggregated ciphertext And the second fog level aggregated ciphertext
- the fog node FN i sends the aggregated data ⁇ CT i , SCT i , ⁇ i ⁇ to the cloud server for storage.
- the control center sends a challenge message to the cloud server.
- the challenge information includes a list of user areas for data analysis and a random matching coefficient sequence.
- the cloud server uses each user area in the user area list stored in it.
- the aggregated data and the random matching coefficient sequence generate corresponding verifiable encrypted aggregated data response information, and send it to the control center.
- the step S4 includes:
- the control center generates challenge information ⁇ L, chal ⁇ and sends it to the cloud server, where L is the user area list, chal is a random matching coefficient sequence of length ⁇ ,
- the cloud server computes the cloud-level aggregated ciphertext, where the cloud-level aggregated ciphertext includes a first aggregated ciphertext component Second aggregate ciphertext component And the third aggregate ciphertext component
- the cloud server calculates the random number based on the random number ⁇ , the random number ⁇ and the cloud-level aggregated ciphertext And random numbers And the signature of the fog-level aggregated ciphertext of the user area in the user area list L Generate the corresponding aggregate signature
- the control center verifies the response information returned by the cloud server to determine the integrity of the encrypted aggregated data. If the verification is passed, the control center decrypts the encrypted aggregated data to obtain the average of the energy consumption data of all challenged users Value and variance.
- step S5 a homomorphic decryption algorithm against key leakage is used to decrypt the response information.
- the step S5 includes:
- the control center calculates the random number based on the random number ⁇ , the random number ⁇ and the cloud-level aggregated ciphertext And random numbers And calculate the aggregate value of the random matching coefficient And verify the validity of the signature through the following equation:
- step S52 If the signature verification in step S61 is passed, use the anti-key leakage homomorphic decryption algorithm to calculate the true number At the bottom is The discrete logarithm under and divide the result by Obtain the sum M of the power consumption data of all users in each user area in the user area list, namely
- the control center uses the private key p 1 to calculate discrete logarithms separately with in Is a bilinear pair, and then calculate the sum of squares of the original data of the smart meter in each user area of the challenge
- the control center calculates the average value:
- the control center calculates the variance:
- each user area is in charge of a fog node.
- the fog node acts as a data aggregation gateway and data repeater.
- the user power consumption data sent from the smart meter in the user area is obtained by the fog node for the first time aggregation.
- the fog-level aggregates the ciphertext and signs the fog-level aggregated ciphertext, and then sends the fog-level aggregated ciphertext and signature information to the cloud server for storage.
- the cloud server stores the aggregated ciphertext data and signature information of different user areas in the database, and provides data query, statistics, and analysis services to the control center of the smart grid system.
Abstract
本发明公开了一种智能电网中抗密钥泄露的加密数据聚合的统计分析方法,每一个用户区域由一个雾节点负责,雾节点充当数据聚合网关和数据中继器的角色,来自本用户区域的智能电表发送的用户电能消费数据由雾节点进行第一次聚合得到雾级聚合密文,并对雾级聚合密文进行签名,然后将雾级聚合密文和签名信息发送到云服务器上进行存储;云服务器将不同用户区域的聚合密文数据和签名信息存储在数据库中,并向智能电网系统的控制中心提供数据查询、统计、分析服务,云服务器能够在不破坏任何单个用户的隐私的前提下向控制中心提供足够的信息,使得控制中心利用这些信息能够计算出指定用户区域中所有用户用电数据的和、平均值和方差。
Description
本发明属于智能电网大数据分析与信息安全保障领域,特别是涉及一种智能电网中抗密钥泄露的加密数据聚合的统计分析方法。
智能电网在传统电网系统的基础上引入了一些新兴的信息技术,如云计算、人工智能、密码学等,在传统的电能传输网之上建立了一个高级电能测量基础设施(Advanced Metering Infrastructure,AMI),用于双向传输电网用户的电能消费数据和智能电网控制中心反馈的控制指令、电价信息等。AMI的核心是一个叫做智能电表的嵌入式设备,它被安装在电网用户的家中,用于周期性地采集电能数据并上报给控制中心,控制中心根据电表采集的细粒度用户用电数据,使用各种数据分析技术从中挖掘有用的信息,根据分析结果监控系统运行情况,然后动态地调整和优化电网系统的发电、配电计划。另一方面,控制中心也可以将一些反馈控制指令、电价信息、停电信息通过数据通道下发给智能电表,电网用户通过与智能电表交互就能获知这些信息,进而调整家庭用电计划。
在采用各种信息技术对用户电能消费数据进行采集和传输的同时,智能电网也引入了各种安全威胁。由于电能数据通过开放的无线网络进行传输,因此在传输过程中,用户的隐私安全、数据机密性、数据完整性都将受到威胁。一方面,可能存在外部敌手对通信信道进行窃听,拦截、替换、篡改用户的用电消费数据,进而破坏用户隐私,导致系统运行混乱;电网用户自身也可能会试图篡改用电数据以逃避后续的用电计费。另一方面,系统中可能存在内部敌手,窃取控制中心的解密私钥,用以解密单个用户的用电数据密文,破坏数据机密性和用户隐私安全。除了这些安全威胁以外,由于智能电网系统中用户量大、数据密度高、高频数据采集与传输、对实时性要求高等特点,如何设计一个高效可验证的数据聚合分析方法是一个关键的问题。
此外,由于电能消费数据通常在智能电表端被使用各种方法进行加密处理以保障数据机密性和用户隐私安全,数据将会丧失不同程度的可用性,导致密文数据在经过聚合后,控制中心通过对聚合结果解密只能够获取有限的统计信息。因此,一个用于智能电网系统的实用的电能消费数据聚合方法必须在保障数据机密性、完整性和用户隐私安全这些基本安全需求的同时,为系统控制中心提供尽可能多的统计分析结果,同时要保证聚合过程、完整性验证过程、聚合数据解密和分析过程尽可能的高效,以满足智能电网对性能的需求。
近年来,国内外学者提出了多种采用各种技术的数据聚合协议。其中,同态加密算法被广泛应用在算法的设计中,由于同态加密算法所具有的保持加法或者乘法的特性,数据被加 密后,能够被高效地进行聚合,同时控制中心可以利用解密私钥直接对聚合密文进行解密,得到一些统计值,而无需对单个用户的密文进行解密,由此有效地保护了用户隐私和数据机密性。在2012年,Lu等人利用Paillier同态加密算法提出了一个面向智能电网的数据聚合协议,在他们的方案中,利用一个超递增序列与加密算法相结合以实现多类型数据的聚合,但是为了保证数据完整性,聚合网关结点需要执行多次耗时的双线性映射操作,这就导致了协议的运算开销较大,不适用于一些对实时性要求高的系统。Chen等人采用Boneh-Goh-Nissim(BGN)同态加密算法提出了一个隐私保护的多功能数据聚合方案,他们利用BGN的乘法同态特性使得控制中心能够通过对聚合密文的解密,获取数据的总和和方差值。
发明内容
本发明的目的在于克服现有技术的不足,提供一种智能电网中抗密钥泄露的加密数据聚合的统计分析方法。
本发明的目的是通过以下技术方案来实现的:智能电网中抗密钥泄露的加密数据聚合的统计分析方法,包括:
S1.系统初始化:可信中心生成该方法中涉及的安全参数,并分配各通信实体的公私钥,所述通信实体包括智能电表、雾节点、云服务器和控制中心;然后可信中心发布安全参数中的公开参数,并通过安全信道将各私钥发送给相应的通信实体;
S2.数据上报:智能电表对采集的用户电能消费数据进行加密得到电表数据密文,将电表数据密文进行签名得到电表数据签名,并将电表数据密文和电表数据签名作为电表数据发送给对应的雾节点进行聚合;
S3.雾级聚合:雾节点接收到预设周期内对应的用户区域中智能电表发送的所有电表数据后,对电表数据中的电表数据签名进行验证,若验证通过,则雾节点对电表数据进行聚合得到雾级聚合密文,并对聚合值进行签名得到雾级密文签名,雾节点将雾级聚合密文和雾级密文签名作为聚合数据发送给云服务器进行存储;
S4.数据分析请求和响应:控制中心向云服务器发送一个挑战信息,所述挑战信息包括进行数据分析的用户区域列表和一个随机匹配系数序列,云服务器使用其存储的用户区域列表中各用户区域的聚合数据和随机匹配系数序列生成对应的可验证的加密聚合数据的响应信息,并发送给控制中心;
S5.验证和解密:控制中心对云服务器返回的响应信息进行验证以确定加密聚合数据的完整性,若验证通过,则控制中心对加密聚合数据进行解密,得到所有挑战的用户电能消费数据的平均值和方差。
优选的,步骤S1中的安全参数包括抗密钥泄露的同态加密算法的安全参数和线性同态数 字签名算法的安全参数。
优选的,步骤S2中智能电表结合随机盲化技术,使用抗密钥泄露的同态加密算法对用户电能消费数据进行加密,步骤S5中使用抗密钥泄露的同态解密算法对响应信息进行解密。
优选的,步骤S3中雾节点对电表数据签名进行验证采用批量验证的方法。
优选的,步骤S1包括:
S11.给定一个安全参数k,可信中心生成抗密钥泄露的同态加密算法的参数(n,g,G,G
T,e),其中e:G×G→G
T是一个双线性映射,G和G
T均为具有合数阶n的群,n=p
1p
2,p
1和p
2均为具有k比特长的大素数,g是群G的一个生成元;可信中心计算控制中心的公钥
S12.可信中心设置一个有限域F
p上的椭圆曲线E,并确定一个基于椭圆曲线E的双线性映射
G
1×G
1→G
2,其中,p是一个大素数,G
1是一个q阶加法循环群,G
2是一个q阶乘法循环群;可信中心选取加法循环群G
1的生成元P,设置智能电网系统中雾节点的个数为N,每个用户区域中智能电表的个数为
可信中心设置两个抗碰撞的安全的哈希函数H
1:{0,1}
*→G
1,
其中,{0,1}
*是任意长度比特串,
是与q互素的剩余类乘法循环群;
S13.可信中心随机选取五个常量α,β,γ,δ,ζ,且α∈Z
n,β∈Z
n,γ∈Z
n,δ∈Z
n,ζ∈Z
n,α·β+γ·δ+ζ=n;可信中心计算公开参数f=g
α和公开参数ε=g
γ,同时为每个雾节点FN
i选取一个签名私钥y
i∈Z
q,并计算该签名私钥y
i∈Z
q对应的签名验证公钥Y
i=y
iP;
S14.对于每个具有唯一身份标识
的智能电表SM
ij,可信中心为其随机选取一个签名私钥y
ij∈Z
q,Z
q是模q剩余类环,智能电表SM
ij为第i个雾结点FN
i下属的第j个智能电表;可信中心计算智能电表SM
ij对应的签名验证公钥Y
ij=y
ijP;可信中心为每个用户区域中的智能电表SM
ij选取随机数π
ij和随机数s
ij,其中,π
ij∈Z
n,s
ij∈Z
n,α·π
ij+γ·s
ij=ζ,
可信中心计算秘密参数
和秘密参数
S15.可信中心通过安全信道将私钥p
1发送给控制中心,将私钥y
ij、秘密参数π
ij和秘密 参数s
ij发送给智能电表SM
ij,将秘密参数π
i、秘密参数s
i和私钥y
i发送给雾结点FN
i。
优选的,步骤S2包括:
S21.对于每个具有唯一身份标识
的智能电表SM
ij,智能电表SM
ij选择一个随机数r
ij∈Z
n,计算电表数据密文
其中,m
ij∈[0,MAX]为用户电能消费数据,MAX是消费数据的最大值,且MAX小于p
2;
优选的,步骤S3包括:
S34.雾结点FN
i对雾级聚合密文进行签名得到雾级密文签名σ
i=(y
i+h
1(CT
i||SCT
i))H
1(ID
CS),其中ID
CS为云服务器的唯一身份标识符;
S35.雾结点FN
i将聚合数据{CT
i,SCT
i,σ
i}发送给云服务器进行存储。
优选的,步骤S4包括:
S45.云服务器将响应信息Agg={σ,h,Y,CT,PCT,SCT}发送给控制中心。
优选的,步骤S5包括:
本发明的有益效果是:
(1)本发明采用抗密钥泄露的同态加密算法对数据进行加密,智能电表使用控制中心的公钥产生密文的过程中采用随机盲化技术,即便控制中心的私钥在某些特殊情况下泄露,也 不会导致单个密文在整个智能电网系统中被解密,可有效实现用户隐私和数据机密性保护;
(2)本方明设计了一个轻量级的批量数据完整性验证技术,使得控制中心可在常量时间复杂度内验证请求的所有加密聚合数据的完整性,而与所请求的区域个数和区域中智能电表的个数无关,可有效确保整个智能电网系统加密数据的完整性;
(3)本方明可以为控制中心提供灵活的数据统计分析查询能力,控制中心或者服务提供商能够选择性地指定感兴趣的用户区域范围,即能够向云服务器指定一个任意的用户区域索引子集用于统计分析;同时,云服务器能够在不破坏任何单个用户的隐私的前提下向控制中心提供足够的信息,使得控制中心利用这些信息能够计算出指定用户区域中所有用户用电数据的和、平均值和方差;
(4)本发明在智能电网用户量与通信数据量很大的情况下,控制中心只需恒定计算量就可以快速验证加密聚合数据的完整性,从而实时地进行加密数据统计分析;
(5)本发明的智能电网中抗密钥泄露的加密数据聚合的统计分析方法基于雾计算架构,利用部署在网络边界的雾结点服务器和云服务器缓解业务系统的计算和存储压力;。
图1为智能电网系统的一种示意图;
图2为本发明的一种流程图。
下面将结合实施例,对本发明的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域技术人员在没有付出创造性劳动的前提下所获得的所有其他实施例,都属于本发明保护的范围。
本发明提供一种智能电网中抗密钥泄露的加密数据聚合的统计分析方法:
如图1-2所示,智能电网中抗密钥泄露的加密数据聚合的统计分析方法,包括:
S1.系统初始化:可信中心生成该方法中涉及的安全参数,并分配各通信实体的公私钥,所述通信实体包括智能电表、雾节点、云服务器和控制中心;然后可信中心发布安全参数中的公开参数,并通过安全信道将各私钥发送给相应的通信实体。
在一些实施例中,步骤S1中的安全参数包括抗密钥泄露的同态加密算法的安全参数和线性同态数字签名算法的安全参数。
所述步骤S1包括:
S11.给定一个安全参数k,可信中心(TTP)生成抗密钥泄露的同态加密算法的参数(n,g,G,G
T,e),其中e:G×G→G
T是一个双线性映射,G和G
T均为具有合数阶n的群, n=p
1p
2,p
1和p
2均为具有k比特长的大素数,g是群G的一个生成元;可信中心计算控制中心的公钥
S12.可信中心设置一个有限域F
p上的椭圆曲线E,并确定一个基于椭圆曲线E的双线性映射
G
1×G
1→G
2,其中,p是一个大素数,G
1是一个q阶加法循环群,G
2是一个q阶乘法循环群;可信中心选取加法循环群G
1的生成元P,设置智能电网系统中雾节点的个数为N,每个用户区域中智能电表的个数为
可信中心设置两个抗碰撞的安全的哈希函数H
1:{0,1}
*→G
1,
其中,{0,1}
*是任意长度比特串,
是与q互素的剩余类乘法循环群。
S13.可信中心随机选取五个常量α,β,γ,δ,ζ,且α∈Z
n,β∈Z
n,γ∈Z
n,δ∈Z
n,ζ∈Z
n,α·β+γ·δ+ζ=n;可信中心计算公开参数f=g
α和公开参数ε=g
γ,同时为每个雾节点FN
i选取一个签名私钥y
i∈Z
q,并计算该签名私钥y
i∈Z
q对应的签名验证公钥Y
i=y
iP。
S14.对于每个具有唯一身份标识
的智能电表SM
ij,可信中心为其随机选取一个签名私钥y
ij∈Z
q,Z
q是模q剩余类环,智能电表SM
ij为第i个雾结点FN
i下属的第j个智能电表;可信中心计算智能电表SM
ij对应的签名验证公钥Y
ij=y
ijP;可信中心为每个用户区域中的智能电表SM
ij选取随机数π
ij和随机数s
ij,其中,π
ij∈Z
n,s
ij∈Z
n,α·π
ij+γ·s
ij=ζ,
可信中心计算秘密参数
和秘密参数
S15.可信中心通过安全信道将私钥p
1发送给控制中心(CC),将私钥y
ij、秘密参数π
ij和秘密参数s
ij发送给智能电表SM
ij,将秘密参数π
i、秘密参数s
i和私钥y
i发送给雾结点FN
i。
S2.数据上报:智能电表对采集的用户电能消费数据进行加密得到电表数据密文,将电表数据密文进行签名得到电表数据签名,并将电表数据密文和电表数据签名作为电表数据发送给对应的雾节点进行聚合。
在一些实施例中,步骤S2中智能电表结合随机盲化技术,使用抗密钥泄露的同态加密算法对用户电能消费数据进行加密,步骤S5中使用抗密钥泄露的同态解密算法对响应信息进行解密。
所述步骤S2包括:
S21.对于每个具有唯一身份标识
的智能电表SM
ij,智能电表SM
ij选择一个随机数r
ij∈Z
n,计算电表数据密文
其中,m
ij∈[0,MAX]为用户电能消费数据,MAX是消费数据的最大值,且MAX小于p
2。
S3.雾级聚合:雾节点接收到预设周期内对应的用户区域中智能电表发送的所有电表数据后,对电表数据中的电表数据签名进行验证,若验证通过,则雾节点对电表数据进行聚合得到雾级聚合密文,并对聚合值进行签名得到雾级密文签名,雾节点将雾级聚合密文和雾级密文签名作为聚合数据发送给云服务器进行存储。
所述步骤S3包括:
S34.雾结点FN
i对雾级聚合密文进行签名得到雾级密文签名σ
i=(y
i+h
1(CT
i||SCT
i))H
1(ID
CS),其中ID
CS为云服务器(CS)的唯一身份标识符。
S35.雾结点FN
i将聚合数据{CT
i,SCT
i,σ
i}发送给云服务器进行存储。
S4.数据分析请求和响应:控制中心向云服务器发送一个挑战信息,所述挑战信息包括进行数据分析的用户区域列表和一个随机匹配系数序列,云服务器使用其存储的用户区域列表中各用户区域的聚合数据和随机匹配系数序列生成对应的可验证的加密聚合数据的响应信 息,并发送给控制中心。
所述步骤S4包括:
S45.云服务器将响应信息Agg={σ,h,Y,CT,PCT,SCT}发送给控制中心。
S5.验证和解密:控制中心对云服务器返回的响应信息进行验证以确定加密聚合数据的完整性,若验证通过,则控制中心对加密聚合数据进行解密,得到所有挑战的用户电能消费数据的平均值和方差。
在一些实施例中,步骤S5中使用抗密钥泄露的同态解密算法对响应信息进行解密。
所述步骤S5包括:
本实施例中每一个用户区域由一个雾节点负责,雾节点充当数据聚合网关和数据中继器的角色,来自本用户区域的智能电表发送的用户电能消费数据由雾节点进行第一次聚合得到雾级聚合密文,并对雾级聚合密文进行签名,然后将雾级聚合密文和签名信息发送到云服务器上进行存储。云服务器将不同用户区域的聚合密文数据和签名信息存储在数据库中,并向智能电网系统的控制中心提供数据查询、统计、分析服务。
本实施例的正确性证明如下:
加密聚合数据完整性验证方程的正确性证明如下:
控制中心计算挑战的各个用户区域的统计和M的正确性证明如下:
控制中心计算挑战的各个用户区域的智能电表原始数据的平方和M
2的正确性证明如下:
以上所述仅是本发明的优选实施方式,应当理解本发明并非局限于本文所披露的形式,不应看作是对其他实施例的排除,而可用于各种其他组合、修改和环境,并能够在本文所述构想范围内,通过上述教导或相关领域的技术或知识进行改动。而本领域人员所进行的改动和变化不脱离本发明的精神和范围,则都应在本发明所附权利要求的保护范围内。
Claims (9)
- 智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,包括:S1.系统初始化:可信中心生成该方法中涉及的安全参数,并分配各通信实体的公私钥,所述通信实体包括智能电表、雾节点、云服务器和控制中心;然后可信中心发布安全参数中的公开参数,并通过安全信道将各私钥发送给相应的通信实体;S2.数据上报:智能电表对采集的用户电能消费数据进行加密得到电表数据密文,将电表数据密文进行签名得到电表数据签名,并将电表数据密文和电表数据签名作为电表数据发送给对应的雾节点进行聚合;S3.雾级聚合:雾节点接收到预设周期内对应的用户区域中智能电表发送的所有电表数据后,对电表数据中的电表数据签名进行验证,若验证通过,则雾节点对电表数据进行聚合得到雾级聚合密文,并对聚合值进行签名得到雾级密文签名,雾节点将雾级聚合密文和雾级密文签名作为聚合数据发送给云服务器进行存储;S4.数据分析请求和响应:控制中心向云服务器发送一个挑战信息,所述挑战信息包括进行数据分析的用户区域列表和一个随机匹配系数序列,云服务器使用其存储的用户区域列表中各用户区域的聚合数据和随机匹配系数序列生成对应的可验证的加密聚合数据的响应信息,并发送给控制中心;S5.验证和解密:控制中心对云服务器返回的响应信息进行验证以确定加密聚合数据的完整性,若验证通过,则控制中心对加密聚合数据进行解密,得到所有挑战的用户电能消费数据的平均值和方差。
- 根据权利要求1所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S1中的安全参数包括抗密钥泄露的同态加密算法的安全参数和线性同态数字签名算法的安全参数。
- 根据权利要求1所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S2中智能电表结合随机盲化技术,使用抗密钥泄露的同态加密算法对用户电能消费数据进行加密,步骤S5中使用抗密钥泄露的同态解密算法对响应信息进行解密。
- 根据权利要求1所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S3中雾节点对电表数据签名进行验证采用批量验证的方法。
- 根据权利要求1所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S1包括:S11.给定一个安全参数k,可信中心生成抗密钥泄露的同态加密算法的参数(n,g,G,G T,e),其中e:G×G→G T是一个双线性映射,G和G T均为具有合数阶n的群, n=p 1p 2,p 1和p 2均为具有k比特长的大素数,g是群G的一个生成元;可信中心计算控制中心的公钥S12.可信中心设置一个有限域F p上的椭圆曲线E,并确定一个基于椭圆曲线E的双线性映射 其中,p是一个大素数,G 1是一个q阶加法循环群,G 2是一个q阶乘法循环群;可信中心选取加法循环群G 1的生成元P,设置智能电网系统中雾节点的个数为N,每个用户区域中智能电表的个数为 可信中心设置两个抗碰撞的安全的哈希函数 其中,{0,1} *是任意长度比特串, 是与q互素的剩余类乘法循环群;S13.可信中心随机选取五个常量α,β,γ,δ,ζ,且α∈Z n,β∈Z n,γ∈Z n,δ∈Z n,ζ∈Z n,α·β+γ·δ+ζ=n;可信中心计算公开参数f=g α和公开参数ε=g γ,同时为每个雾节点FN i选取一个签名私钥y i∈Z q,并计算该签名私钥y i∈Z q对应的签名验证公钥Y i=y iP;S14.对于每个具有唯一身份标识 的智能电表SM ij,可信中心为其随机选取一个签名私钥y ij∈Z q,Z q是模q剩余类环,智能电表SM ij为第i个雾结点FN i下属的第j个智能电表;可信中心计算智能电表SM ij对应的签名验证公钥Y ij=y ijP;可信中心为每个用户区域中的智能电表SM ij选取随机数π ij和随机数s ij,其中,π ij∈Z n,s ij∈Z n, 可信中心计算秘密参数 和秘密参数S15.可信中心通过安全信道将私钥p 1发送给控制中心,将私钥y ij、秘密参数π ij和秘密参数s ij发送给智能电表SM ij,将秘密参数π i、秘密参数s i和私钥y i发送给雾结点FN i。
- 根据权利要求6所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S3包括:S34.雾结点FN i对雾级聚合密文进行签名得到雾级密文签名σ i=(y i+h 1(CT i||SCT i))H 1(ID CS),其中ID CS为云服务器的唯一身份标识符;S35.雾结点FN i将聚合数据{CT i,SCT i,σ i}发送给云服务器进行存储。
- 根据权利要求7所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S4包括:S45.云服务器将响应信息Agg={σ,h,Y,CT,PCT,SCT}发送给控制中心。
- 根据权利要求8所述的智能电网中抗密钥泄露的加密数据聚合的统计分析方法,其特征在于,步骤S5包括:
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