CN110825955A - A Distributed Differential Privacy Recommendation Method Based on Location Services - Google Patents

Abstract

The invention discloses a distributed differential privacy recommendation method based on location-based services, which can solve the problems that the traditional recommendation system cannot be well adapted to the location-based recommendation services and privacy disclosure is faced. The method utilizes a distributed privacy protection recommendation framework and a differential privacy protection theory to design a singular value decomposition recommendation algorithm based on the distributed framework, and utilizes an order-preserving encryption function to realize the protection of the position requested by a user, thereby achieving the purpose of privacy protection.

Description

A Distributed Differential Privacy Recommendation Method Based on Location Services

technical field

The present invention relates to the field of recommendation systems and privacy protection, in particular to a distributed differential privacy recommendation method based on location services.

Background technique

With the rapid development of mobile Internet and intelligent terminal technology, Location-based Service (LBS) has been widely researched and applied. At present, mobile users can use GPS technology in smart terminals to perceive their geographic location, and at the same time, by sending their location information to LBS service providers, they can request personalized services from service providers, such as the most common point of interest recommendation, map Navigation etc.

When users obtain personalized services, they need to provide their own location information to the service provider. At the same time, the service provider will calculate the user's preferences according to the user's historical consumption records, and recommend the user's potential interest from a large number of items. Items that require user location constraints. Therefore, in this process, users are faced with two threats of privacy leakage: one is the leakage of user geographic location privacy; the other is the risk of leakage of user preference information.

At present, privacy protection methods for location-based recommendation systems are mainly divided into three types: generalization, data perturbation, and encryption. The generalization of the user's location information has the problem of low security in the face of new attacks; the data perturbation party has the problem of insufficient protection capability; the homomorphic encryption algorithm also has high computational complexity and is applied in large-scale data sets. Recommend inefficient problems.

SUMMARY OF THE INVENTION

Aiming at the traditional recommendation system that cannot adapt to the location-based recommendation service, and also faces the problem of privacy leakage, the present invention realizes a distributed privacy protection recommendation framework, and uses the differential privacy protection theory to design a distributed framework-based recommendation framework. Singular value decomposition recommendation algorithm, and use order-preserving encryption function to protect the user's requested location.

In order to achieve the above purpose, the technical solution adopted in the present invention is: a distributed differential privacy recommendation method based on location service, comprising the following steps:

Step S11, forming a distributed recommendation system architecture to perform privacy protection on historical scoring data and location privacy information;

Step S12, the formed distributed recommendation system architecture uses the cloud computing service mode, and the user's rating information is stored in the recommendation servers of each cloud after being processed by distributed protection;

Step S13, adding noise to realize differential privacy protection;

Step S14, realize order-preserving encryption through four functions of Gen, Der, Enc and Cmp;

Step S15, the user terminal executes an unconstrained random slicing algorithm;

Step S16, the user terminal executes a constrained hierarchical random slicing algorithm;

Step S17, sending the shard score to each distributed recommendation server, and executing the user's recommendation request in the second stage;

Step S18, executing the input perturbation stochastic gradient descent algorithm to obtain the user and item latent feature vector matrices P _k ^m×f and Q _k ^n×f added with privacy protection;

In step S19, the privacy protection model of the location server side implements the privacy protection of the location request service.

In the step S11, the distributed recommendation system architecture is mainly constructed based on the singular value decomposition method, and the model is as formula 1:

where Test represents the training set of user u's evaluation set for item _i , p _u and qi represent the latent factor eigenvalue vectors of users and items, Ψ represents the template function, r represents the predicted rating variable, p represents the user latent eigenfactor variable, q represents the item latent eigenfactor variable, T represents the matrix transpose, and λ represents the regularization parameter.

In the step S12, the operation process of the distributed recommendation system architecture includes:

并在每份数据上添加基于差分隐私的干扰噪声发送给每个推荐服务器；1) First, the user _ui scores the recommended item _{poij after consumption r ij ,} and then executes the random slicing algorithm to divide the score into K parts according to the number of distributed recommendation servers

And add interference noise based on differential privacy on each piece of data and send it to each recommendation server;

和采用公式2：2) After the distributed recommendation server k receives the scoring shard data, it periodically executes the gradient descent algorithm according to the objective function in formula 1 to update the latent factor eigenvalue vectors of users and items

and Using Equation 2:

3) When the user _ui requests the point-of-interest recommendation service, he obtains his own geographic coordinates ( _xi , _yi ) through the intelligent terminal positioning, and then sets his own address request interval ( _xi -Δx _i1 ) according to the user's request range requirements. , x _i +Δx _i2 ), (y _i -Δy _i1 , y _i +Δy _i2 ) are sent to the location server, and the location server selects the recommended items that meet the user's request by matching the geographic location of the recommended items, and sends them to the distribution The recommendation server sends a rating prediction request;

4) After the distributed recommendation server receives the request from the location server, it calculates the predicted score through the eigenvalue vector of the latent factor of the user and the item, using formula 3:

Each distributed recommendation server sends its own shard prediction score to the user, and the user calculates

In the step S14, the function expression is as follows:

Gen function: Given a security parameter k and a range parameter n, k∈N and n∈N, by inputting k and n, Gen outputs an encryption parameter param and a master key mkey, where: (param, mkey)=Gen( k, n);

Enc function: Given parameter param and master key mkey, input plaintext num, the function can output ciphtext ciph, ciph=Enc(param, mkey, num);

Der function: Given parameter param and master key mkey, input plaintext num, this function can generate token token, token=Der(param, mkey, num);

Cmp function: Given the parameter param, two ciphertexts ciph and ciph', and the token token, the function can output {-1, 0, 1}, Cmp(param, ciph, ciph', token) ∈ {-1, 0, 1};

Given the ciphtext ciph=Enc(param, mkey, num) and ciph′=Enc(param, mkey, num′), the secret comparison can be realized by the Cmp function;

In the step S15, the random sharding algorithm randomly divides the score r _ij into K parts according to the number K of the distributed recommendation servers and adopts the principle of no constraint, and sends them to the DRS accordingly.

In the step 16, according to the number K of distributed recommendation servers, the random sharding algorithm divides the score r _ij into K parts according to the ratio set by the user, and sends them to DRS accordingly.

且满足

In the step S13, according to the Laplace mechanism, noise is first added to the score data, where the global sensitivity of the score Δr=r _max -r _min , then the added noise is Laplace(Δr/ε), and then random sharding is performed on the user end. Algorithm, after sending the scoring shard data to each DRS, each DRS will get a user-item shard scoring matrix

and satisfy

The processing method of the step S19 is as follows:

1) User _ui first generates security parameters k and n, and uses Gen function to generate encryption parameter param and comparison key mkey; then request the range ( _xi -Δx _i1 , x _i +Δx _i2 ), (y _i - Δy _i1 , y _i +Δy _i2 ) are encrypted to obtain Enc(x _i -Δx _i1 , x _i +Δx _i2 ), Enc(y _i -Δy _i1 , y _i +Δy _i2 ), Der(x _i -Δx _i1 , x _i +Δx _i2 ) and Der(y _i -Δy _i1 , y _i +Δy _i2 ), user _ui sends these encrypted data together with param and mkey to LBSS;

2) After the location server receives the user's location request, it performs a comparable encryption protocol to screen interest points. First, the location server traverses all interest points, and the geographic coordinates (lon _j , lat _j ) of each interest point poi _j will satisfy the The points of interest of the screening conditions are added to the set to be recommended _RP , and the specific comparison conditions are as follows:

The location server sends the _POI numbers in the set RP to be recommended to the DRS, and requests the DRS to perform prediction and recommendation;

并将每个预测评分分片发送给用户RU；3) After each DRS receives the recommendation prediction request from the location server, it executes

and send each prediction score shard to user RU;

并从中选择Top-N个评分最高的推荐结果。4) After the user receives the rating from the recommendation server, execute

And select the Top-N recommendation results with the highest ratings.

The invention innovatively proposes a distributed differential privacy recommendation method based on location service, which comprehensively considers the overall rights and interests, while ensuring the recommendation performance, has strong privacy protection ability, and has certain contributions to academic research and practical applications. .

Description of drawings

Below is a brief description of the content expressed by each drawing in the description of the present invention:

1 is a flowchart of a method for constructing a research on a distributed differential privacy recommendation method based on location services disclosed in an embodiment of the present invention;

FIG. 2 is an architecture diagram of a distributed system disclosed by an embodiment of the present invention;

Fig. 3 is the experimental data diagram of Ctrip Beijing hotel disclosed in the embodiment of the present invention;

FIG. 4 is a data diagram of Beijing food experiment data of Dianping.com disclosed in the embodiment of the present invention.

Detailed ways

Below with reference to the accompanying drawings, through the description of the embodiments, the specific implementation of the present invention, such as the shape and structure of each component involved, the mutual position and connection relationship between each part, the function and working principle of each part, and the manufacturing process and operation and use methods, etc., are described in further detail to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solutions of the present invention.

The present invention proposes a distributed privacy protection recommendation framework. In order to prevent the leakage of users' historical rating data and location privacy information, the present invention uses a distributed recommendation system architecture to realize the privacy protection of the above two kinds of information, and uses a cloud computing service model. , the user's rating information is processed by distributed protection and stored in the recommendation servers of each cloud. Using the differential privacy protection theory, a singular value decomposition recommendation algorithm based on a distributed framework is designed, and the order-preserving encryption function is used to protect the user's requested location. The invention uses a comparable encryption scheme, and the query result can be obtained through one round of interaction. At the same time, it can also satisfy the user's location security. The scheme is realized by four functions: Gen, Der, Enc and Cmp. The present invention proposes an unconstrained random slicing algorithm and a constrained hierarchical random slicing algorithm, and an example verifies the performance of each slicing algorithm.

In order to further improve the security of the distributed privacy protection framework, the present invention integrates the differential privacy protection method on the basis of the random fragmentation algorithm, and adds noise, so as to ensure that in the case of the collusion of the distributed recommendation server, a better performance can be achieved. Privacy protection capability: The user and item latent eigenvector matrices P _k ^m×f and Q _k ^n×f with added privacy protection are obtained through the input perturbation stochastic gradient descent algorithm, and a comparable encryption scheme is adopted between the user and the location server.

As shown in Figure 1, the specific implementation is as follows:

Step S11 , in order to prevent the leakage of the user's historical rating data and location privacy information, the present invention uses a distributed recommendation system architecture to realize the privacy protection of the above two kinds of information.

In step S12, the distributed structure uses a cloud computing service mode, and the user's rating information is stored in the recommendation servers of each cloud after being processed by distributed protection.

Step S13, adding noise to realize differential privacy protection.

Step S14, order-preserving encryption is a query encryption scheme for solving the range query without revealing the query value. The present invention uses a comparable encryption scheme, and the query result can be obtained through one round of interaction, and at the same time, the security of the user's location can be satisfied. , through Gen, Der, Enc and Cmp four functions.

Step S15, the user terminal executes an unconstrained random slicing algorithm.

Step S16, the user terminal executes a constrained hierarchical random slicing algorithm.

Step S17: Send the shard score to each distributed recommendation server, and execute the user's recommendation request in the second stage.

Step S18: Execute the input perturbation stochastic gradient descent algorithm to obtain the user and item latent feature vector matrices P _k ^m×f and Q _k ^n×f added with privacy protection.

Step S19, the privacy protection model on the location server side, realizes the privacy protection of the location request service, and analyzes the demonstration result.

The distributed privacy protection recommendation framework in step S11 is mainly based on the singular value decomposition (SVD) method, which can efficiently process large-scale data sets. Compared with the traditional collaborative filtering method, its performance is better. A large advantage, its model is as formula (1).

In the above formula, Text represents the training set of user u's evaluation set for item _i , and p _u and qi represent the latent factor eigenvalue vectors of users and items. Ψ is the objective function, and the optimal solution can be obtained by the gradient descent optimization algorithm.

Based on the system architecture in Figure 2, the running process of each object entity is as follows:

1) First, the user _ui scores the recommended item _{poij after consumption r ij ,} and then executes the random slicing algorithm to divide the score into K parts according to the number of distributed recommendation servers And add interference noise based on differential privacy on each piece of data and send it to each recommendation server.

和

2) After the distributed recommendation server k receives the scoring shard data, it periodically executes the gradient descent algorithm according to the objective function in formula (1) to update the latent factor eigenvalue vectors of users and items

and

3) When the user _ui requests the point-of-interest recommendation service, he obtains his own geographic coordinates ( _xi , _yi ) through the intelligent terminal positioning, and then sets his own address request interval ( _xi -Δx _i1 ) according to the user's request range requirements. , x _i +Δx _i2 ), (y _i -Δy _i1 , y _i +Δy _i2 ) are sent to the location server, and the location server selects the recommended items that meet the user's request by matching the geographic location of the recommended items, and sends them to the distribution The recommendation server sends a rating prediction request.

4) After the distributed recommendation server receives the request of the location server, it calculates the predicted score through the eigenvalue vector of the latent factor of the user and the item:

Each distributed recommendation server sends its own shard prediction score to the user, and the user calculates

Order Preserving Encryption (OPE) is a query encryption scheme for solving range queries without revealing the query value. The present invention uses a comparable encryption scheme to obtain query results through one round of interaction.

At the same time, it can also meet the guarantee of user location security. The scheme is implemented by four functions Gen, Der, Enc and Cmp, the specific functions are as follows:

Gen function: Given a security parameter k and a range parameter n, k∈N and n∈N, by inputting k and n, Gen outputs an encryption parameter param and a master key mkey. which is:

param, mkey) = Gen(k, n) (4)

Enc function: Given the parameter param and the master key mkey, input the plaintext num, the function can output the ciphertext ciph.

ciph=Enc(param, mkey, num) (5)

Der function: Given the parameter param and the master key mkey, and input the plaintext num, this function can generate the token token.

token=Der(param, mkey, num) (6)

Cmp function: Given the parameter param, two ciphertexts ciph and ciph', and the token token, the function can output {-1, 0, 1}.

Cmp(param, ciph, ciph', token) ∈ {-1, 0, 1} (7)

Given the ciphtext ciph=Enc(param, mkey, num) and ciph′=Enc(param, mkey, num′), the secret comparison can be realized by the Cmp function.

Further, the distributed privacy protection recommendation method is divided into two stages. The first stage executes the sharding algorithm on the user side, and the matrix factorization algorithm is executed on each distributed recommendation server side to update the potential feature factors of users and items; the second stage Execute the user's referral request. Assuming that the user _ui 's score for the post-consumption recommended item poij is r _ij , the sharding algorithm is executed on the user side, and then the _shard score is sent to each distributed recommendation server. The present invention proposes two random fragmentation algorithms:

Random sharding algorithm 1 randomly divides the score r _ij into K parts according to the number K of distributed recommendation servers and adopts the principle of unconstrained, and sends them to DRS accordingly. The specific algorithm is as algorithm 1.

Algorithm 1 Unconstrained random sharding algorithm

Step 1. Input the user rating r _ij and the number of slices K.

Step 2. Generate a random number between (0, r _ij ) and assign it to the variable r.

Step 3. Compare the variables r and r _ij -r, and select the number with the smallest value as the slice score.

Step 4. Repeat the above steps until scoring r _ij K slices.

用户将该参数作为私密信息保存，在后续的分片算法中采用该比例参数；然后根据比例参数分割评分r_ij，具体步骤见算法2。According to the number K of distributed recommendation servers, the random sharding algorithm 2 adopts the principle of proportional constraint, divides the score r _ij into K parts according to the proportion set by the user, and sends them to DRS accordingly. The specific steps are that the user first randomly initializes K scale parameters {w ₁ , w ₂ , . . . , w _K } , and satisfies the

The user saves the parameter as private information, and uses the proportional parameter in the subsequent sharding algorithm; and then divides the score r _ij according to the proportional parameter, see Algorithm 2 for specific steps.

Algorithm 2 Constrained random sharding algorithm

Step 1. Input the user rating r _ij and the number of slices K.

Step 2. Generate K random numbers between (0, 1) {w ₁ , w ₂ , ..., w _K }, and make them satisfy

Step 3. Multiply each random number {w ₁ , w ₂ , . . . , w _K } by the user score r _ij to obtain K slice scores.

In order to further improve the security of the distributed privacy protection framework, the present invention integrates the differential privacy protection method on the basis of the random fragmentation algorithm, so as to ensure that the distributed recommendation server can also achieve better privacy protection capability in the case of collusion . According to the Laplace mechanism, the present invention firstly adds noise to the scoring data, where the global sensitivity of the scoring is Δr=r _max -r _min , then the added noise is Laplace (Δr/ε), and then a random fragmentation algorithm is executed on the user side to assign the scoring After the shard data is sent to each DRS, each DRS will get a user-item shard score matrix and satisfy

通过算法3可以得到添加了隐私保护的用户和项目潜在特征向量矩阵P_k ^m×f和Q_k ^n×f。What DRS actually acquires is a slice matrix with interference noise added. Let the rating matrix obtained by the kth DRS actually be

Through Algorithm 3, the potential feature vector matrices P _k ^m×f and Q _k ^n×f of users and items with added privacy protection can be obtained.

Algorithm 3 Stochastic Gradient Descent with Perturbation Added

Step 1. Input the slice score matrix R′ _k added with Laplace noise, the dimension f of the latent factor matrix, the regularization parameter λ, and the maximum value of the score r _max .

Step 2: Control the scoring matrix R′ _k with added noise in the range of [0, r _max ].

利用随机梯度下降算法进行矩阵因子分解，计算出用户和项目特征向量矩阵P_k ^m×f和Q_k ^n×f；Step 3. According to the objective function

Use stochastic gradient descent algorithm to decompose the matrix, and calculate the user and item eigenvector matrices P _k ^m×f and Q _k ^n×f ;

In actual use, each DRS periodically executes the IPSGD algorithm after receiving the user’s shards, and updates the P _k ^m×f and Q _k ^n×f matrices, so it can be predicted through the user and item latent factor eigenvalue vector-valued matrices Other shard scores, namely:

The location server mainly stores the geographic location coordinates of each point of interest, and accepts location service requests from users. In order to avoid the leakage of the user's location privacy, this section adopts a comparable encryption scheme between the user and the location server, and the privacy protection protocol for implementing the location request service is as follows:

1. (@RU): User u _i first generates security parameters k and n, and uses Gen function to generate encryption parameter param and comparison key mkey; then request the range (x _i -Δx _i1 , x _i +Δx _i2 ) , (y _i -Δy _i1 , y _i +Δy _i2 ) are encrypted to obtain Enc(x _i -Δx _i1 , x _i +Δx _i2 ), Enc(y _i -Δy _i1 , y _i +Δy _i2 ), Der( x _i -Δx _i1 , x _i Δx _i2 ) and Der(y _i -Δy _i1 , y _i +Δy _i2 ), user _ui sends these encrypted data together with param and mkey to LBSS.

2. (@LBSS): After the location server receives the user's location request, it performs the operation of filtering points of interest through comparable encryption protocols. First, the location server traverses all points of interest, the geographic coordinates (lon _j , lat _j ) of each point of interest _poij , and adds the points of interest that meet the screening conditions to the set to be recommended _RP . The specific comparison conditions performed are as follows:

The location server sends the point of interest numbers in the set _RP to be recommended to the DRS, and requests the DRS to perform predictive recommendation.

3. (@DRS): After each DRS receives the recommendation prediction request from the location server, it executes And send each predicted score shard to user RU.

4. (@RU): After the user receives the rating from the recommendation server, execute And select the Top-N recommendation results with the highest ratings.

Finally, the present invention selects the following four algorithms to compare with the model proposed by the present invention:

(1) UBCF Model: This model uses a user-based collaborative filtering method to predict user items' ratings, and does not have the function of privacy protection.

(2) IBCF Model: This model adopts the item-based collaborative filtering method to achieve the scoring prediction of user items, and does not have the function of privacy protection.

(3) SVD Model: This model obtains the latent factor eigenvalue vectors of users and items through matrix factorization technology, and realizes the scoring prediction of user items. This model does not have privacy protection features.

(4) DP-SVD Model: Based on the SVD recommendation model, this model applies differential privacy technology to add Laplace noise to the user-item rating matrix to achieve the purpose of protecting the privacy of user ratings while recommending, but does not have the A feature that protects the user's geographic location.

(5) DDP-SVD Model: The distributed privacy protection model proposed by the present invention not only protects the privacy of user scores, but also protects the geographic location of users.

The present invention adopts two domestic well-known website data sets for verification and analysis, namely Beijing hotel data from Ctrip.com and Beijing food data from Dianping.com. These two data sets are data captured online by web crawlers, including The user's evaluation of the item (evaluation level is divided into 1 to 5), the geographic coordinates of the item. After cleaning the hotel data of Ctrip and the food data of Dianping.com, the data with sparse scores is filtered out, and the data that meets the test requirements of the present invention are screened out, as shown in Figures 3 and 4 .

In the present invention, firstly, based on the knowledge background of mobile internet location service, it is considered that traditional recommendation system can not adapt well to location-based recommendation service, and also faces the problem of privacy leakage; secondly, a distributed privacy protection recommendation is proposed. framework, design a singular value decomposition recommendation algorithm based on a distributed framework, and use the order-preserving encryption function to protect the user's requested location; finally, the differential privacy protection theory is creatively added to effectively achieve privacy protection and achieve a good recommendation effect. The invention effectively improves the privacy protection capability, so that the obtained comprehensive performance can reach a better level.

Professionals can further realize that the execution steps described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, computer software or a combination of the two. Whether these functions are implemented by hardware or software depends on Specific applications and design constraints for technical solutions. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

The present invention has been exemplarily described above in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited by the above methods, as long as various insubstantial improvements made by the method concept and technical solutions of the present invention are adopted, or no improvement is made. It is within the protection scope of the present invention to directly apply the concepts and technical solutions of the present invention to other occasions.

Claims (8)

1. A distributed differential privacy recommendation method based on location service, characterized in that it comprises the following steps: Step S11, forming a distributed recommendation system architecture to perform privacy protection on historical scoring data and location privacy information; Step S12, the formed distributed recommendation system architecture uses the cloud computing service mode, and the user's rating information is stored in the recommendation servers of each cloud after being processed by distributed protection; Step S13, adding noise to realize differential privacy protection; Step S14, realize order-preserving encryption through four functions of Gen, Der, Enc and Cmp; Step S15, the user terminal executes an unconstrained random slicing algorithm; Step S16, the user terminal executes a constrained hierarchical random slicing algorithm; Step S17, sending the shard score to each distributed recommendation server, and executing the user's recommendation request in the second stage; Step S18, executing the input perturbation stochastic gradient descent algorithm to obtain the user and item latent feature vector matrices P _k ^m×f and Q _k ^n×f added with privacy protection; In step S19, the privacy protection model of the location server side implements the privacy protection of the location request service. 2. The distributed differential privacy recommendation method based on location service according to claim 1, wherein: in the step S11, the distributed recommendation system architecture is mainly constructed based on the singular value decomposition method, and the model is as formula 1:

where Test represents the training set of user u's evaluation set for item _i , p _u and qi represent the latent factor eigenvalue vectors of users and items,

Represents Frobenius normal form, and Ψ represents a template function. 3. The distributed differential privacy recommendation method based on location service according to claim 1 or 2, characterized in that: in the step S12, the distributed recommendation system architecture operation process comprises: 1) First, the user _ui scores the recommended item _{poij after consumption r ij ,} and then executes the random slicing algorithm to divide the score into K parts according to the number of distributed recommendation servers

And add interference noise based on differential privacy on each piece of data and send it to each recommendation server; 2) After the distributed recommendation server k receives the scoring shard data, it periodically executes the gradient descent algorithm according to the objective function in formula 1 to update the latent factor eigenvalue vectors of users and items

and

Using Equation 2:

3) When the user _ui requests the point of interest recommendation service, he obtains his own geographic coordinates (x _i , y _i ) through the intelligent terminal positioning, and then sets his own address request interval (x _i -Δx _i1 ) according to the user's request range requirements. ,x _i +Δx _i2 ), (y _i -Δy _i1 ,y _i +Δy _i2 ) are sent to the location server, and the location server selects the recommended items that meet the user's request by matching the geographic location of the recommended items, and sends them to the distribution The recommendation server sends a rating prediction request; 4) After the distributed recommendation server receives the request from the location server, it calculates the predicted score through the eigenvalue vector of the latent factor of the user and the item, using formula 3:

Each distributed recommendation server sends its own shard prediction score to the user, and the user calculates

4. The distributed differential privacy recommendation method based on location service according to claim 3, is characterized in that: in described step S14, function expression is as follows: Gen function: Given a security parameter k and a range parameter n, k∈N and n∈N, by inputting k and n, Gen outputs an encryption parameter param and a master key mkey, where: (param,mkey)=Gen( k,n); Enc function: Given parameter param and master key mkey, input plaintext num, the function can output ciphtext ciph, ciph=Enc(param,mkey,num); Der function: Given parameter param and master key mkey, input plaintext num, this function can generate token token, token=Der(param,mkey,num); Cmp function: Given the parameter param, two ciphertexts ciph and ciph′ and the token token, the function can output {-1,0,1}, Cmp(param,ciph,ciph′,token)∈{-1, 0,1}; Given the ciphtext ciph=Enc(param,mkey,num) and ciph′=Enc(param,mkey,num′), the secret comparison can be realized by the Cmp function;

5. The distributed differential privacy recommendation method based on location service according to claim 4, characterized in that: in the step S15, the random sharding algorithm adopts the unconstrained principle according to the number K of distributed recommendation servers, The score r _ij is randomly divided into K parts and sent to the DRS accordingly. 6. The distributed differential privacy recommendation method based on location services according to claim 5, wherein in the step 16, the random fragmentation algorithm adopts the principle of equal ratio constraint according to the number K of distributed recommendation servers, The score r _ij is divided into K parts according to the ratio set by the user, and sent to the DRS accordingly. 7 . The distributed differential privacy recommendation method based on location services according to claim 6 , wherein in the step S13 , noise is first added to the score data according to the Laplace mechanism, wherein the global sensitivity of the score Δr=r _max 7 . -r _min , then the added noise is Laplace (Δr/ε), and then the random sharding algorithm is executed on the user side. After sending the score shard data to each DRS, each DRS will get a user-item shard score matrix and satisfy

8. The distributed differential privacy recommendation method based on location service according to claim 1, 2, 4, 5, 6 or 7, characterized in that: the processing method of step S19 is as follows: 1) User _ui first generates security parameters k and n, and uses Gen function to generate encryption parameter param and comparison key mkey; then request range ( _xi -Δx _i1 , x _i +Δx _i2 ), (y _i - Δy _i1 , y _i +Δy _i2 ) for encryption to obtain Enc(x _i -Δx _i1 ,x _i +Δx _i2 ), Enc(y _i -Δy _i1 ,y _i +Δy _i2 ), Der(x _i -Δx _i1 , x _i +Δx _i2 ) and Der(y _i -Δy _i1 , y _i +Δy _i2 ), user _ui sends these encrypted data together with param and mkey to LBSS; 2) After the location server receives the user's location request, it performs a comparable encryption protocol to filter the points of interest. First, the location server traverses all the points of interest. The geographic coordinates (lon _j , lat _j ) of each point of interest poi _j will satisfy the The points of interest of the screening conditions are added to the set to be recommended _RP , and the specific comparison conditions are as follows:

The location server sends the _POI numbers in the set RP to be recommended to the DRS, and requests the DRS to perform prediction and recommendation; 3) After each DRS receives the recommendation prediction request from the location server, it executes and send each prediction score shard to user RU; 4) After the user receives the rating from the recommendation server, execute

And select the Top-N recommendation results with the highest ratings.

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