JP4350399B2 - Information disclosure program - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an information disclosure program for publishing various information held by various companies and organizations in response to an information disclosure request.
[0002]
[Prior art]
In various organizations such as various companies and government offices, when there is a request for information disclosure from an unspecified number of people, information that does not interfere with the disclosure of the various information that it owns is disclosed. . However, in the disclosure of information, it is necessary to pay attention to the leakage of privacy and confidential information at the same time as the necessary information is disclosed.
[0003]
2. Description of the Related Art In recent years, information disclosure over the Internet has become common for information publishers such as various companies and various organizations such as government offices in consideration of convenience of information disclosure.
[0004]
As for security for data stored on the database of the information disclosure apparatus of the information publisher, research is being conducted as database security as described in Non-Patent Document 1, for example. Access control for controlling access rights to a database and flow control for preventing unauthorized copying of data between users with different access rights are relatively known techniques.
[0005]
In addition, inference control for preventing non-public information from being inferred using previously held knowledge and public statistical information has been researched and developed. For example, when the number of pieces of information on which statistical information is based falls below a certain number (public reference value K), there is a method of not releasing statistical information. These methods assume the case where a human accesses a DBMS (database management system) from a terminal or accesses a public site using a browser.
[0006]
However, when data exchange between computers using a general-purpose data format such as XML (eXtensible Markup Language) becomes widespread, it is expected that cases in which public data is read by a computer will be generalized. In this case, the public data is read collectively.
[0007]
For example, for the purpose of market research for product development in a company, the case of examining the number of people in a specific age group of a specific gender living in a specific region by searching a public site in a local government information disclosure device is verified. In this case, using the public information of the local government, for example, “number of women over 26” is searched while gradually narrowing the district, that is, gradually increasing the level of detail of the information. First, the entire city is searched, then XX town, and then each XX town is searched in turn.
[0008]
It is possible to easily infer various kinds of non-public information by repeatedly performing such a search in large quantities or at regular intervals using a computer.
[0009]
At this time, in order to prevent the leakage of privacy due to the inference of market researchers, as described above, if the “number of women over 26” is less than the public reference value (K = 10) such as 10 people, the result is obtained. Measures such as not answering have been implemented.
[0010]
For example, in the case of inhabitant information, the actual inhabitant information (number of women over 26) read from the database and aggregated is
As of March 1, 2001 XX Town 1-chome: 15 people, 2-chome: 20 people, 3-chome: 35 people
As of March 1, 2002 XX Town 1-chome: 9 people, 2-chome: 15 people, 3-chome: 35 people
It is assumed that the public reference value K is 10 or more.
[0011]
In this case, as of 1st March 2002, there are 9 people in 1 chome, so the question limited to 1 chome will not return a result. Measures will be taken, such as opening up a total of 24 people as the number of people in 1-2. Therefore,
Public data as of March 1, 2001 is
XX town 1 chome: 15 people, 2 chome: 20 people, 3 chome: 35 people,
The public data as of March 1, 2002 is
XX town 1-2 chome: 24 people, 3 chome: 35 people.
[0012]
[Non-Patent Document 1]
Silvana Castano et.al. “DATABASE SECURITY”, Addison-Wesley Publishing Company ACM Press, 1994 p17-18
[0013]
[Problems to be solved by the invention]
However, as described above, when the number of pieces of information serving as the basis of statistical information falls below a certain number (public reference value K), the information disclosure method that prohibits the disclosure of statistical information still solves the problem. There were the following problems.
[0014]
That is, as described above, in order to prevent inference, the degree of detail of public data is changed according to the number of data. However, since the search processing is performed on public data by a computer, it is easy to repeatedly read out data at regular intervals. As a result, when the change in the detail level is observed with the transition of time, it is easily inferred that the number of data at a certain point in time is less than a certain number.
[0015]
For example, in the example above,
Public data as of March 1, 2001 is
XX town 1-chome: 15 people, 2-chome: 20 people, 3-chome: 35 people,
The public data as of March 1, 2002 is
XX town 1-2 chome: 24 people, 3 chome: 35 people.
[0016]
In this case, since the 1st and 2nd streets were merged for the first time in the public data as of March 1, 2002, it became clear that the public reference value K was 14 or more. Since the current number of 1st and 2nd streets has been added up, the number of women aged 26 or older in 1st and 2nd streets is 14 or less, and the other is 24-14 = 10. Can be easily inferred.
[0017]
In this way, when public data is exchanged between the computer on the searcher side and the computer on the information publisher side, a large amount of searches or repeated searches over time can be easily performed. Therefore, it is necessary for the information publisher to suppress the disclosure of detailed data in anticipation of various inferences.
[0018]
The present invention has been made in view of such circumstances. Even in an environment where information can be retrieved by a computer and continuous information can be easily read out, privacy and confidentiality by inference can be achieved. An object of the present invention is to provide an information disclosure program that can prevent leakage of information and can perform more than a certain amount of detailed information disclosure.
[0019]
[Means for Solving the Problems]
In order to solve the above-described problems, the present invention provides an information disclosure program for causing a computer to realize a function of reading data stored in a database in response to a search request and disclosing it as a search response. , Search A function that randomly sets the public reference value of the number of data in the response over time, Search Only when the number of data in the response is greater than the public reference value Above Search request Search against A function to return a response is realized.
[0020]
In the information disclosure program configured as described above, the disclosure reference value based on data changes randomly with time. Therefore, at the time of the previous search execution, for example, the number of data of the A section classified by the attribute value of the specified item and the number of data of the B section were individually disclosed. Even if the number of data that is the sum of the number of data in section A and the number of data in section B is disclosed by combining the section B and section B, How much less I can't infer that it has fallen. Therefore, it is possible to prevent the privacy and security due to inference from being impaired.
[0021]
Furthermore, in another information disclosure program described above, the computer realizes a function of setting a lower limit value of the disclosure based on the number of data and a function of setting an upper limit value of the disclosure based on the number of data. Furthermore, the function of setting the disclosure reference value randomly sets the disclosure reference value at random between the lower limit value and the upper limit value as time elapses.
[0022]
In this way, by changing the public reference value between the lower limit value and the upper limit value, it is possible to prevent inference for a negative question.
[0023]
Another invention employs a value obtained by subtracting the lower limit value from the total number of data stored in the database to the upper limit value in the information disclosure program described above.
[0024]
Another invention relates to a computer, A search condition was specified Search request By The database Specified in the search condition from the database. In an information disclosure program for realizing the function of reading out the number of data for each category classified by the value of an item and publishing it as a search response, A function of setting a public reference value of the number of data in the search response; Stored in the database Target of the search response The total number of data for each category Above A function of combining randomly and a function of responding by incorporating the combined number of data and the total number of data into the search response so as to be equal to or greater than the public reference value and less than the upper limit value are realized.
[0025]
In the information disclosure program configured as described above, the respective categories stored in the database are randomly combined so that the total number of data is greater than or equal to the disclosure reference value and less than the upper limit value. Further, the disclosure standard value based on the number of data is fixed, but when the number of data becomes less than the disclosure standard value, the result is combined with other categories, and the total number of data becomes equal to or greater than the disclosure standard value.
[0026]
Furthermore, since the combination including one category is changed, it cannot be inferred that the number of data in the specific category has dropped below the quasi-value. Therefore, like the information disclosure program of the previous invention, it is possible to prevent the privacy and security due to inference from being impaired.
[0027]
In another invention, in the above-described information disclosure program, a value obtained by subtracting the disclosure reference value from the addition value of the number of data of all the categories stored in the database is adopted as the disclosure reference value as the upper limit value. .
[0028]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
(First embodiment)
FIG. 1 is a block diagram showing a schematic configuration of an information disclosure apparatus to which an information disclosure program according to the first embodiment of the present invention is applied.
[0029]
The information disclosure apparatus 1 according to the first embodiment receives a search request designating a search condition from the searcher terminal 2 possessed by a searcher (computer) requesting information disclosure via the Internet 3. 4 is read out and transmitted as a search result (search response) to the searcher terminal 2 that is the search request source via the Internet 3.
[0030]
In the database 4, as shown in FIG. 2 (a), the survey date, address, name, sex, and age are stored for each inhabitant.
[0031]
FIG. 2B shows aggregated data indicating the result of the public data generation unit 14 searching the database 4 using the search conditions of the search request in response to the search request and totaling the number of residents that match the search condition. FIG.
[0032]
As shown in the conceptual diagram of the total data in FIG. 2B, the disclosure date 5 of the current information that matches the search condition, the search condition 6, each category 7, and the number of data (number of people) 8 in each category 7 are totaled. ing. Each section 7 is configured in a tree form. In this embodiment, the maximum unit is “city”, the intermediate unit is “town”, and the minimum unit is “chome”. Note that as the category 7, it is possible to combine categories of the same unit.
[0033]
In FIG. 1, the setting unit 9 sets the disclosure lower limit value Ks for the number of data 8 of each section 7 in the lower limit value storage unit 11 in accordance with an instruction from a setting person 10, such as an administrator of the information disclosure apparatus 1. To do. In this embodiment, the lower limit value Ks for disclosure of the number of data 8 in each section 7 of the minimum unit “chome” is set. Each section 7 of the number of data 8 less than the lower limit value is not open to the public. Further, the setting unit 9 follows the instruction from the setter 10 to update the public reference value Ka. ₀ Is set in the public reference value update period storage unit 12.
[0034]
The clock circuit 13 measures the current time t (year, month, day, hour, minute) and sends it to the public data generation unit 14. The last update time storage unit 15 stores the last update time t in the public reference value Ka. ₀ Remember. The random number generation unit 16 generates a public reference value Ka that satisfies the condition of exceeding the lower limit value Ks and less than the upper limit value (n−Ks) as a random number, and sends it to the public data generation unit 14.
[0035]
Ks <Ka <(n−Ks)
Note that n in the upper limit (n−Ks) is a value obtained by adding the number of data 8 of each division 7 to be disclosed this time. For example, in the aggregated data read and compiled from the database 4 shown in FIG. 2B, there are 6 divisions 7 of the minimum unit “chome” to be disclosed this time, and the 6 data number 8 is added. The value n is 112.
[0036]
When a search request specifying a search condition is input via the accepting unit 17, the public data generation unit 14 reads data corresponding to the search condition stored in the database (DB) 4 and needs it together with the number thereof. These are processed accordingly and output as a search result (search response) via the receiving unit 17.
[0037]
An information disclosure program 19 is stored in a storage device 18 such as an HDD provided in the information disclosure device 1 constituted by a kind of information processing device such as a computer. The information disclosure apparatus 1 such as a computer implements the above-described units 9, 11, 12, 14, 15, and 16 by starting the information disclosure program 19.
[0038]
The public data generation unit 14 realized in the information disclosure apparatus 1 executes public data generation processing according to the flowchart shown in FIG.
When a search specifying a search condition is input via the accepting unit 17 (S1), the database 4 is searched with the search condition including the input category 7, and data of all the categories 7 matching the search condition is read out. Create statistical data. Number of data in Category 7 (D ₁ ~ D _N ) Is obtained (S2). Then, the last update time t stored in the last update time storage unit 15 ₀ Update interval T stored in the public reference value update interval storage unit 12 ₀ The time (t ₀ + T ₀ ) Reaches the current time t of the clock circuit 13 (S3),
(T ₀ + T ₀ ) <T
Update the current public reference value Ka.
[0039]
That is, the number of data 8 (D ₁ ~ D _N ) Is calculated (S4), and the random number generator 16 calculates the value as described above.
Ks <Ka <(n−Ks)
A public reference value Ka that is a random number satisfying the above is generated. When a new public reference value Ka is generated, the current time t is changed to the last update time t. ₀ Is written in the last update time storage unit 15 (S5).
[0040]
And the number of data 8 (D ₁ ~ D _N ) Are each greater than or equal to the public reference value Ka (S6). At S3, the current time t is updated to the update time (t ₀ + T ₀ ), The process immediately proceeds to S6, and the number of data 8 (D ₁ ~ D _N ) Are each equal to or greater than the public reference value Ka.
[0041]
Number of data in one division 7 (D ₁ ~ D _N However, if there are 8 data items less than the public reference value Ka (S6), the target category 7 and the category 7 adjacent to the target category 7 are combined to form a new category 7, and the data of the target category 7 The sum of the number 8 of data in the section 7 adjacent to the number 8 is set to be equal to or greater than the public reference value Ka. If the total number of data 8 does not exceed the public reference value Ka even if the two sections 7 are combined, the adjacent sections 7 are further merged (S7).
[0042]
When the number of data 8 for each category 7 or the number 8 of data for the category 7 of the combination is equal to or greater than the public reference value Ka, the number of data 8 for each category 7 or the number of data 8 for the category 7 Edit as public data (S8). The edited public data is returned as a search result to the searcher terminal 2 via the receiving unit 17 (S9).
[0043]
In the information disclosure apparatus 1 to which the information disclosure program of the first embodiment configured as described above is applied, the disclosure reference value Ka of the number of data 8 of the category 7 specified by the search condition is a constant update interval T. ₀ Each time, the random number generator 16 is used to change randomly under the condition of Ks <Ka <(n−Ks).
[0044]
Therefore, when the previous search execution, the number of data 8 of the specific section 7 was disclosed alone, but at the time of the current search execution, the specific section 7 is combined with another adjacent section 7, Even if the total number of data 8 is disclosed, it cannot be inferred that the number 8 of data in a specific category 7 has decreased below. Therefore, it is possible to prevent the privacy and data security due to inference from being impaired.
[0045]
Also, the public reference value Ka is randomly changed between the lower limit value Ks and the upper limit value (n−Ks). Furthermore, n is an added value of the number of data 8 of all the divisions 7 to be disclosed, out of the upper limit value (n−Ks). Therefore, it is possible to prevent the disclosure reference value Ka from increasing unrestricted and the information disclosure requester (searcher) from sufficiently disclosing necessary information (data).
[0046]
(Second Embodiment)
FIG. 4 is a block diagram showing a schematic configuration of an information disclosure apparatus to which the information disclosure program according to the second embodiment of the present invention is applied. The same parts as those of the information disclosure apparatus 1 to which the information disclosure program according to the first embodiment shown in FIG. 1 is applied are denoted by the same reference numerals, and detailed description of the overlapping parts is omitted.
[0047]
In the information disclosure apparatus 1a of the second embodiment, a database 4 and a reception unit 17 having the same configuration as the information disclosure apparatus 1 of the first embodiment are provided. Further, in the information disclosure apparatus 1a, the setting unit 9 stores the disclosure reference value Ka of the number 8 of data in each category 7 according to the instruction of the setter 10 composed of, for example, an administrator of the information disclosure apparatus 1a. Set to unit 20. The disclosure reference value Ka in the information disclosure apparatus 1a of the second embodiment is a fixed value.
[0048]
In the public data generation unit 14a, a subset Xi generation unit 21 that generates a subset Xi having all the categories 7 specified by the search condition in the database 4 as elements, and each of the generated subsets Xi A set Y creating unit 22 that creates a set Y as an element, and a public candidate designating unit 23 that designates a combination of each category 7 that can be disclosed from the set Y created by the set Y creating unit 22 as a public candidate. A public candidate memory 24 for temporarily storing the public candidates is provided.
[0049]
Then, the public data generation unit 14a randomly combines and combines the sections 7 stored in the database 4 so that the total number of data is greater than or equal to the public reference value ka and less than the upper limit value (n−Ka). The section 7 and the total number of data 8 are incorporated into the search response and responded.
[0050]
An information disclosure program 19a is stored in a storage device 18 such as an HDD provided in an information disclosure device 1a constituted by a kind of information processing device such as a computer. The information disclosure apparatus 1a such as a computer implements the above-described units 9, 14a, 20, 21, 22, 23, and 24 by starting the information disclosure program 19a.
[0051]
Then, the public data generation unit 14a realized in the information disclosure apparatus 1a executes public data generation processing according to the flowchart shown in FIG.
When a search request specifying a search condition is input via the receiving unit 17 (Q1), the database 4 is searched with the search condition including the input category 7, and data of all the categories 7 matching the search condition is read. Statistical data is created and the number of data in Category 7 is 8 (D ₁ ~ D _N ) Then, an added value (total number of data) n is calculated by adding the number of data 8 of all the sections 7 matching the search condition (Q2). Next, the disclosure candidate memory 24 is cleared (Q3).
[0052]
Then, a subset Xi having all the segments 7 designated by the search condition in the database 4 as elements is created (Q4).
A method of creating this subset Xi will be described. For example, assume that the number of categories 7 specified by the search condition is N. In this case, 0 to (2 in N digits. ^N -Generate a binary number of 1), and generate a set of elements (subset Xi) with the subscript as a subscript. i = 0 to (2 ^N -1) That is, 2 that are all combinations in each of N divisions 7 ^N Subsets X0 to X (2 ^N ―1) is created.
[0053]
Here, if the value of the a-th digit of the generated i-th binary number is 1, the subset Xi is a subset of the target section 7 including the a-th data. For example, when N, that is, the number of divisions 7 is 10,
X0 = [0000000000], X1 = [0000000001],
X2 = [0000000010], X3 = [0000000011],
. . . X (2 ^N ―1) = [1111111111]
2 to the 10th power is generated. For example, X3 represents [0000000011] and is a subset including the ninth section 7 and the tenth section 7.
[0054]
Next, all subsets X0 to X (2 of X ^N ―1) elements Y1 to Y2 ^N A set Y is generated (Q5).
[0055]
And about the element Yk of the set Y (however, k = 1-2) ^N First, k is set to an initial value of 1 (k = 1) (Q6). Next, a total sum Sm (total number of data) of the number of data 8 of each section 7 set to “1” included in the subset Xm constituting the element Yk is obtained (Q7). And the sum Sm is
Ka <Sm <n-Ka
(Q8), the combination of each section 7 included in the subset Xm and the sum Sm (total number of data) of the number 8 of data in each section 7 are added to the public candidate memory 24 (Q9). . When there is one section 7 set to “1” included in the subset Xm constituting the element Yk, the number of data 8 of this one section 7 is regarded as the sum Sm and the above relationship is determined. To do.
[0056]
Next, k is updated (k = k + 1) (Q10), and the updated k is 2 ^N (Q11), the process returns to Q7, and the sum Sm of the number of data 8 of each section 7 set to “1” included in the subset Xm constituting the element Yk (the total number of data) )
[0057]
K after update is 2 ^N Exceeds (Q11), 2 included in set Y ^N Since the verification for the combinations of the respective sections 7 in all the subsets Xi has been completed, the combinations of the respective sections 7 written in the open candidate memory 24, the respective sections 7 alone, and the corresponding total sum Sm (the total number of data ), The single data number 8 is returned as a search result (Q12).
[0058]
In this method, not all the categories 7 specified by the search condition are included in the disclosure candidates. In order to include all, in Q8, in addition to “greater than Ka (less than n−Ka)”, a condition is added that the logical sum of all elements Xm of Yk becomes a binary number of all 1. That's fine.
[0059]
In the information disclosure apparatus 1a to which the information disclosure program of the second embodiment configured as described above is applied, for example, 2 in N categories 7 specified by the search condition. ^N 2 for all combinations of types ^N Subsets Xi (i = 0-2) ^N -Prepared as 1). And this 2 ^N When the total value (total Sm) of the data number 8 of the corresponding combination satisfies the condition of “greater than public reference value Ka (less than n−Ka)” for all combinations of the types, this combination and total value (total Sm).
[0060]
Thus, since one category 7 is simultaneously incorporated into a plurality of combinations at the same time, each combination to which the relevant category 7 belongs at the time of the previous search and each combination to which the relevant category 7 belongs at the time of the current search are not necessarily Even if the number of data of each previous combination and the number of data of each combination of this time are compared and contrasted, it cannot be inferred that the number of data 8 of the corresponding category 7 has decreased below the public reference value Ka. Therefore, it is possible to prevent the privacy and security due to inference from being impaired.
[0061]
(Third embodiment)
FIG. 6 is a flowchart showing the operation of the public data generation unit incorporated in the information disclosure apparatus to which the information disclosure program of the third embodiment of the present invention is applied. The same parts as those in the flowchart showing the operation of the public data generation unit 14a incorporated in the information disclosure apparatus 1a according to the second embodiment shown in FIG. Further, the overall configuration of the information disclosure apparatus is substantially the same as that of the information disclosure apparatus 1a of the second embodiment shown in FIG.
[0062]
In the information disclosure apparatus according to the third embodiment, among N sections 7 specified by the search condition included in the search request, M sections (M <N) that are determined in advance according to various circumstances are included. Only the number of data 8 is to be disclosed.
[0063]
In the flowchart of FIG. 6, when a search request is input (Q1), the database 4 is searched with the search condition including the input category 7, and the data of all the categories 7 that match the search condition is read and statistical data is created. And the number of data in category 7 (D ₁ ~ D _N ) Then, an addition value (total number of data) n is calculated by adding the number of data 8 of all N sections 7 that match the search condition (Q2). Next, the disclosure candidate memory 24 is cleared (Q3).
[0064]
Then, a subset Xi having M (M <N) predetermined divisions 7 among N divisions 7 designated by the search condition in the database 4 as an element is created (Q4a).
A method of creating this subset Xi will be described. For example, assume that the number of categories 7 specified by the search condition is N. In this case, 0 to (2 in M digits ^M -A binary number of 1) is generated, and a set of elements Xi with the subscript as a subscript is generated. i = 0 to (2 ^M ―1) That is, all combinations in the M divisions 7 of the disclosure target are 2 ^M Subsets X0 to X (2 ^M ―1) is created.
[0065]
Here, the subset Xi is a subset of the target section 7 including the b-th data if the value of the generated i-th binary b digit is 1. For example, when M, that is, the number of categories 7 to be disclosed is 3, and the items to be disclosed are the first category 7, the third category 7, and the fifth category 7,
X0 = [000], X1 = [001], X2 = [010], X3 = [011],
. . X (2 ^M ―1) = [111]
A total of eight binary numbers of 2 to the power of 2 are generated. For example, X3 represents [011] and is a subset including the third section 7 and the fifth section 7 to be disclosed.
[0066]
Next, all subsets X0 to X (2 of X ^M ―1) elements Y1 to Y2 ^M A set Y is generated (Q5a).
[0067]
And about the element Yk of the set Y (however, k = 1-2) ^M First, k is set to an initial value of 1 (k = 1) (Q6). Next, the sum Sm (total number of data) of the number of data 8 of each section 7 to be disclosed set to “1” included in the subset Xm constituting the element Yk is obtained (Q7). And the sum Sm is
Ka <Sm <n-Ka
(Q8), the combination of each section 7 included in the subset Xm and the sum Sm (total number of data) of the number 8 of data in each section 7 are added to the public candidate memory 24 (Q9). . In addition, when the number of disclosure target sections 7 set to “1” included in the subset Xm constituting the element Yk is one, the number of data 8 of the one section 7 is regarded as the sum Sm and the above. Determine the relationship.
[0068]
k is updated (k = k + 1) (Q10), and the updated k is 2 ^M Is confirmed (Q11a), the process returns to Q7, where the sum Sm of the number of data 8 for each division 7 to be disclosed set to “1” included in the subset Xm constituting the element Yk (summation) Data number).
[0069]
K after update is 2 ^M (Q11a), 2 included in set Y ^M Since the verification for the combination of each section 7 to be published in all the subsets Xi is completed, the combination of each section 7 to be published written in the publication candidate memory 24, each section 7 alone, and the corresponding sum Sm (total number of data) and single data number 8 are returned as search results (Q12).
[0070]
In this method, not all the categories 7 specified by the search condition are included in the disclosure candidates. In order to include all, in Q8, in addition to “greater than Ka (less than n−Ka)”, a condition is added that the logical sum of all elements Xm of Yk becomes a binary number of all 1. That's fine.
[0071]
In the information disclosing apparatus to which the information disclosing program of the third embodiment configured as described above is applied, M (M <N) pre-designated out of N sections 7 designated by the search condition. 2 for all combinations of Category 7 ^M Subsets Xi (i = 0-2) ^M -Prepared as 1). And this 2 ^M When the total value (total Sm) of the data number 8 of the corresponding combination satisfies the condition of “greater than public reference value Ka (less than n−Ka)” for all combinations of the types, this combination and total value (total Sm).
[0072]
Therefore, it is possible to achieve substantially the same operational effects as the information disclosure apparatus 1a to which the information disclosure program of the second embodiment described above is applied.
[0073]
In addition, each component demonstrated by each said embodiment may be combined freely, and may be divided | segmented into a some element. The information disclosure programs 19 and 19a in the embodiment may be distributed on a plurality of computers and operate in cooperation with each other.
[0074]
Further, the information disclosure programs 19 and 19a can be applied to a computer by being transmitted through a communication medium. The operation of the computer that has read the information disclosure programs 19 and 19a is controlled by the information disclosure programs 19 and 19a, thereby realizing the functions described above.
[0075]
【The invention's effect】
As described above, in the information disclosure program of the present invention, the disclosure standard value is changed randomly with the passage of time, or the combination of classifications when the disclosure standard value falls below the result is changed randomly. Yes.
[0076]
Therefore, even in an environment where information can be retrieved by a computer and continuous information can be easily read, privacy and confidential information leakage due to inference can be prevented, and more than a certain amount of detailed information can be obtained. Can be published.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a schematic configuration of an information disclosure apparatus to which an information disclosure program according to a first embodiment of the present invention is applied.
FIG. 2 is a view showing stored contents and aggregated data of a database incorporated in the information disclosure apparatus of the embodiment;
FIG. 3 is a flowchart showing the operation of a public data generation unit incorporated in the information disclosure apparatus of the embodiment;
FIG. 4 is a block diagram showing a schematic configuration of an information disclosure apparatus to which an information disclosure program according to the second embodiment of the present invention is applied.
FIG. 5 is a flowchart showing the operation of a public data generation unit incorporated in the information disclosure apparatus of the embodiment;
FIG. 6 is a flowchart showing the operation of the public data generation unit incorporated in the information disclosure apparatus to which the information disclosure program of the second embodiment of the present invention is applied.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1,1a ... Information disclosure apparatus, 2 ... Searcher terminal, 3 ... Internet, 4 ... Database, 7 ... Classification, 8 ... Number of data, 9 ... Setting part, 10 ... Setting person, 11 ... Lower limit storage part, 12 ... Public reference value update interval storage unit, 13 ... clock circuit, 14, 14a ... public data generation unit, 15 ... last update time storage unit, 16 ... random number generation unit, 17 ... reception unit, 18 ... storage device, 19, 19a ... Information disclosure program, 20 ... public reference value storage unit, 21 ... subset Xi creation unit, 22 ... set Y creation unit, 23 ... public candidate designation unit, 24 ... public candidate memory

Claims (5)

In an information disclosure program for causing a computer to realize a function of reading data stored in a database in response to a search request and publishing it as a search response,
On the computer,
A function of randomly setting a public reference value of the number of data in the search response as time passes;
Information disclosure program, characterized in that to achieve a function of the number of data in the search response returns a search response against the search request only if at least the public reference value. A computer, a function of setting a lower limit value of the data number of the public in the search response, to realize a function of setting the upper limit of the number of data published in the search response,
Wherein the ability to set a random public reference value, the public reference value between the upper limit and the lower limit value, information disclosure of claim 1, wherein the set randomly over time program.   3. The information disclosure program according to claim 2, wherein the upper limit value is a value obtained by subtracting the lower limit value from the total number of data stored in the database. The computer searches the database by specifying a search condition search request, from the database, thereby realizing the functions to be exposed as search response the number of data for each classified by the value of the specified item classification reads that in the retrieval condition In the information disclosure program for
On the computer,
A function of setting a public reference value of the number of data in the search response;
Each segment of interest of the search response stored in the database, as summed the number of data is less than the the public reference value or more and the upper limit value, a function of combining randomly,
An information publishing program that realizes a function of incorporating and combining the combination classification and the total number of data into the search response.   5. The information disclosure program according to claim 4, wherein the upper limit value is a value obtained by subtracting the disclosure reference value from an addition value of the number of data of all sections stored in the database.

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