プライバシー保護した分散データマイニングの通信コスト削減手法
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(2) Vol.2016-DPS-168 No.4 Vol.2016-SPT-21 No.4 Vol.2016-EIP-74 No.4 2016/11/17. ใॲཧֶձڀݚใࠂ IPSJ SIG Technical Report. ૿ՃΛ͑Δ͜ͱ͕Ͱ͖Δɽ͜ΕʹΑΓɼࢄσʔλϚΠ. ߹ΘͤΛ૬ؔϧʔϧͱͯ͠நग़͢Δ͜ͱΛߟ͑Δɽঢ়. χϯά͕Ͱ͖Δରͷ֦େͱࢄσʔλϚΠχϯάͷੑೳ. ͦͷ ID ͳͲͷσʔλϚΠχϯάʹ༻͍ΔσʔλͷཁૉΛ. ͷ্ʹߩظ͕ݙͰ͖Δɽ. ΞΠςϜͱͼݺɼ1 ͭҎ্ͷΞΠςϜͷू߹ΛΞΠςϜηο. ୈ̎અͰɼࢄσʔλϚΠχϯάʹؔ͢Δؔ࿈ڀݚͷ. τͱͿݺɽ͕ऀױૌ͑ͨঢ়ͷཤྺ (ΞΠςϜηοτ) Λτ. આ໌ͱʹ͍ͭͯड़Δɽୈ̏અͰɼఏҊख๏ͷઆ. ϥϯβΫγϣϯͱͿݺɽ֤ҩӃτϥϯβΫγϣϯσʔλ. ໌ͱఏҊΞϧΰϦζϜʹ͍ͭͯड़Δɽୈ̐અͰɼఏҊ. ϕʔεΛอ͓࣋ͯ͠Γɼऀױͷঢ়ͷཤྺ (ΞΠςϜηο. ΞϧΰϦζϜͷੑೳධՁͷ݁ՌΛड़ɼ࠷ͱ·ʹޙΊͱ͠. τ) ΛτϥϯβΫγϣϯͱͯ͠େྔʹอଘ͍ͯ͠Δɽ֤ҩ. ͯ݁Λड़Δɽ. Ӄಉ͡छྨͷঢ়Λѻ͏ͷͱ͠ɼঢ়ͷ ID ڞ௨͠. 2. ؔ࿈ڀݚ جຊతͳࢄσʔλϚΠχϯάΞϧΰϦζϜͱͯ͠ɼ. ͍ͯΔͷͱ͢Δɽ ͜ͷσʔλϚΠχϯάͰɼϓϥΠόγʔอޢͷͨΊɼ ͋ΔҩӃͰঢ়Λૌ͑ͨऀױͷΛଞͷҩӃʹΒΕͳ. FDM[2] ͕͛ڍΒΕΔɽ͜ͷڀݚෳͷσʔλϕʔεΛ. ͍Α͏ʹ૬ؔϧʔϧΛநग़͢ΔɽϓϥΠόγʔͷఆٛઅ. ରͱͨ͠ ͰڥApriori ΞϧΰϦζϜ [3] Λ༻͍ͯڞ௨. 3.2 ʹޙड़͢Δɽ. ͢Δ૬ؔϧʔϧΛநग़͢Δɽ͔͠͠ɼFDM σʔλϚΠ. γεςϜϞσϧΛਤ 1 ʹࣔ͢ɽγεςϜʹࢀՃ͢Δҩྍ. χϯάʹ༻͍ΔࣗϊʔυͷϓϥΠϕʔτσʔλͰ͋ΔΞΠ. ؔػΛϊʔυͱͯ͠ѻ͏ɽϊʔυͷ૯Λ M ͱ͢Δɽ֤. ςϜηοτͱαϙʔτͦͷͷΛଞϊʔυૹ৴ͯ͠͠. ϊʔυʹ 0 ͔Β M − 1 ·Ͱͷ ID ׂ͕ΓৼΒΕ͓ͯΓɼ. ·͏ͨΊɼใྲྀग़ͷݥةੑ͕ଘࡏ͢Δɽ. ϊʔυͷ ID Λ iɼϊʔυΛ Ni (0 ≤ i < M ) ͱ͢هɽ·. FDM ͷ ϓ ϥ Π ό γ ʔ อ ޢΛ ՝ ͱ ͠ ͨ ͯ ͠ ͱ ڀ ݚ. ͨɼN0 Λཧऀϊʔυͱͯ͠ѻ͍ɼଞΛࢀՃऀϊʔυͱ. SFDM[4] ͕͛ڍΒΕΔɽ͜ͷͰڀݚɼσʔλϚΠχ. ͢Δɽϊʔυ Ni ͕࣋ͭσʔλϕʔεΛ DBi ͱද͢ɽ·. ϯάʹ༻͍ΔΞΠςϜηοτͱαϙʔτΛ҉߸Խ͢Δ͜. ͨɼDBi ʹ֨ೲ͞ΕΔ֤τϥϯβΫγϣϯΛ Ti,j ͱ͠ɼ. ͱͰϓϥΠόγʔอޢΛߦ͍ͬͯΔɽ͔͠͠ɼ҉߸ԽΛߦ. −1 DBi = {Ti,1 ɼTi,2 ɼʜ}ɼDB = ∪M i=0 DBi ͱ͢Δɽϊʔυ. ͏ͨΊʹߴ͍ࢉܭίετ͕ඞཁͱͳΔͨΊɼॲཧ͕࣌ؒ૿. Ni ͕࣋ͭɼཁૉͷ͕ k ͷҙͷΞΠςϜηοτΛ Xi,k ɼ. Ճͯ͠͠·͏ɽ. Xi,k ʹର͢ΔαϙʔτΛ Vi,k ͱද͢ɽXi,k ʹ·ؚΕΔ. ࢄσʔλϚΠχϯάʹ͓͚ΔϓϥΠόγʔอࢉܭͱޢ. ΞΠςϜͷΛ Xi,k ͷ͞ͱͼݺɼٻࡏݱΊ͍ͯΔසग़. ίετΛ՝ͱͨͯ͠͠ͱڀݚɼCRDM[1] ͕͋Δɽ͜ͷ. ΞΠςϜηοτͷ͞Λ len ͱද͢ɽΞΠςϜηοτ͕ස. ͰڀݚɺΞΠςϜηοτͷαϙʔτΛϥϯμϜͳͰ. ग़ͱ͞ΕΔج४Λද͢࠷αϙʔτΛ min sup ͱද͢ɽ. ׂ͠ɼଞϊʔυΛܦ༝͔ͤͯ͞ΒूܭΛߦ͏ൿີࢄ๏. min sup Ҏ্ͷαϙʔτΛ࣋ͭΞΠςϜηοτΛසग़ͱ. Λ༻͍Δɽ͜ΕʹΑΓɼ҉߸ԽͳͲͷෛՙͷॏ͍ॲཧΛߦ. ͢Δɽ. ΘͣʹϓϥΠόγʔอޢΛߦ͏͜ͱ͕Ͱ͖Δɽ·ͨɼϊʔ. ҩྍؔػͷσʔλϚΠχϯάΛྫͱ͢Δɽ֤ҩӃɼ. υؒͷ݁ୗʹΑΔใ࿙Ӯʹର͢Δੑ (݁ୗੑ) ͕ߴ. ԬපӃΛ N0 ɼୌҩӃΛ N1 ͷΑ͏ʹද͞هΕΔɽ·ͨɼ. ͘ɼใ࿙ӮͷͨΊʹ߈ܸରͷϊʔυҎ֎ͷϊʔυ͢. N0 σʔλϕʔε DB0 ɼN1 σʔλϕʔε DB1 Λ࣋. ͯͱ݁ୗ͢Δඞཁ͕͋Δɽ͔͠͠ɼϓϥΠόγʔอޢͷ. ͪɼσʔλϕʔεʹ͕ऀױૌ͑ͨঢ়ͷཤྺ͕֨ೲ͞Ε. ͨΊʹଟͷ௨৴͕ඞཁͱͳΔɽ͜ͷͨΊɼଳҬͷ͍ڱ. ͍ͯΔɽΞΠςϜ “಄௧” “͜ݞΓ”ͷΑ͏ʹද͠ɼΞ. ͰڥσʔλϚΠχϯάΛߦͬͨ߹ɼॲཧ͕࣌ؒ૿େ͢Δ. ΠςϜηοτ X1,2 ={ ಄௧ɼ͜ݞΓ } ͷΑ͏ʹද͢ه. Մೳੑ͕ଘࡏ͢Δɽ. Δɽ͜ͷͱ͖ɼΞΠςϜηοτ X1,2 ͷ͞ 2 ͱͳΔɽ. 3. ϓϥΠόγʔอͨ͠ޢࢄσʔλϚΠχϯ άʹ͓͚Δ௨৴ίετͷݮख๏. min sup = 0.05 ͱͨ͠ͱ͖ɼσʔλϕʔεͰ 5%Ҏ্ͷ ׂ߹Ͱग़͢ݱΔΞΠςϜηοτΛɼසग़ΞΠςϜηοτͱ ͯͯ͢͠நग़͢Δ͜ͱ͕తͱͳΔɽ. CRDM Λ༻͍ͨࢄσʔλϚΠχϯάʹɼଟͷ௨৴ ͕ඞཁͱͳΔɽຊͰڀݚɼCRDM σʔλϚΠχϯά ʹࢀՃ͢Δϊʔυͷ૿ՃʹΑͬͯɼաͳϓϥΠόγʔ อޢੑೳΛఏʹͱ͜͏·ͯ͠͠ڙ͠ɼར༻ऀͷཁʹٻ ߹Θͤͯ݁ୗੑΛઃఆͰ͖ΔΑ͏ʹΞϧΰϦζϜΛม ߋ͢Δ͜ͱʹΑͬͯɼ௨৴ίετͷݮΛਤΔɽ. 3.1 γεςϜϞσϧ શࠃʹࡏ͢Δҩྍͯ͜͠ྗڠ͕ؔػͷγεςϜΛ༻͍ ͯɼऀױͷૌ͑ͨঢ়ͷཤྺ͔Βซൃ͍͢͠ঢ়ͷΈ. ⓒ 2016 Information Processing Society of Japan. ਤ 1 ࢄσʔλϚΠχϯάͷγεςϜϞσϧ. 2.
(3) Vol.2016-DPS-168 No.4 Vol.2016-SPT-21 No.4 Vol.2016-EIP-74 No.4 2016/11/17. ใॲཧֶձڀݚใࠂ IPSJ SIG Technical Report. 3.2 ϓϥΠόγʔͷఆٛ ্ड़ͨ͠γεςϜϞσϧͷͱͰɼ֤ϊʔυͰͨ͠ࢉܭ ΞΠςϜηοτͷαϙʔτΛൿີσʔλͱͯ͠ɼϓϥΠ όγʔอޢͷର͢Δɽ ϓϥΠόγʔอޢख๏ͱͯ͠ൿີࢄ๏Λ༻͍Δɽൿີ ࢄ๏ʹର͢Δ߈ܸख๏ͷ̍ͭͱͯ͠ɼ֤ϊʔυ͕݁ୗ͠ ͯ߈ܸରϊʔυͷׂͨ͠ൿີσʔλΛͯ͢ೖख͢Δ ͜ͱ͕ߟ͑ΒΕΔɽ͜ͷ݁ୗʹΑΔ߈ܸʹର͢ΔੑΛධ Ձ͢ΔͨΊʹɼ݁ୗੑΛϓϥΠόγʔอޢੑೳͷࢦඪ ͱͯ͠༻͍Δɽ݁ୗੑͱɼ߈ܸऀ͕߈ܸϊʔυͷൿ. ਤ 2. ࢄσʔλϚΠχϯάͷॲཧͷྲྀΕ (ൿີࢄॲཧ). ີσʔλΛೖख͢ΔͨΊʹɼ݁ୗ͕ඞཁͳϊʔυͷͷ͜ ͱͰ͋Δɽ ྫͱͯ͠ɼ߈ܸϊʔυ A ͕ϊʔυ B ͷαϙʔτΛख ʹೖΕΔͨΊʹϊʔυ C ͱϊʔυ D ͱͷ݁ୗ͕ඞཁͰ͋ Δ߹ɼϊʔυ B ͷ݁ୗੑ 2 ͱͳΔɽ ຊఏҊɼSemi-Honest ηΩϡϦςΟϞσϧ [5] Λ࠾༻ ͢Δɽ͜ΕɼϢʔβ͕γεςϜΛվ͟Μ͢Δ͜ͱͳ͘ɼ γεςϜϓϩτίϧʹଇͬͯར༻͢Δ͜ͱΛࣔ͢ϞσϧͰ ͋Δɽͨͩ͠ɼϢʔβγεςϜ͔Βೖྗͱग़ྗσʔλΛ ੳ͢Δ͜ͱʹΑͬͯɼଞϊʔυͷϓϥΠϕʔτσʔλͷ औಘΛࢼΈΔ͜ͱ͕Ͱ͖Δɽ. 3.3 CRDM ๏ͷࢄσʔλϚΠχϯά. ਤ 3 ࢄσʔλϚΠχϯάͷॲཧͷྲྀΕ (ूॲܭཧ). ͷ͞Λ k ͱ͢Δͱɼසग़ΞΠςϜηοτީิͷαϙʔτ. CRDM ͷൿີࢄ๏Λ༻͍ͨࢄσʔλϚΠχϯάͷ. ͷࢉܭɼൿີࢄ๏Λ༻͍ͨूܭɼ͞ k ͷසग़ΞΠς. ॲཧͷྲྀΕΛਤ 2 ͱਤ 3 ʹࣔ͢ɽਤ 2 Ͱɼ͡Ίʹ֤. Ϝηοτͷੜɼ͞ k + 1 ͷසग़ΞΠςϜηοτީิͷ. ࢀՃऀϊʔυ Ni ɼαϙʔτΛஅยσʔλͱͯ͠ M − i. ੜΛ܁Γฦ͢͜ͱͰͯ͢ͷසग़ΞΠςϜηοτΛੜ. ݸͷϥϯμϜͳͰׂ͢Δɽͦͷޙɼׂͨ͠அยσʔ. ͢Δɽ. λΛͦΕͧΕࣗΑΓ ID ͷߴ͍ϊʔυૹ৴͢Δɽ. ্هͷΞϧΰϦζϜʹΑͬͯɼϓϥΠόγʔอͨ͠ޢ. ਤ 3 Ͱɼૹ৴͞Ε͖ͯͨஅยσʔλͱࣗͷͨΊͷஅ. ࢄσʔλϚΠχϯάΛߦ͏͜ͱ͕Ͱ͖Δɽ͔͠͠ɼൿີ. ยσʔλ (Vi,k,1 ) Λͯ͢Ճࢉ͠ɼཧऀϊʔυૹ৴͢. ࢄॲཧʹଟͷ௨৴͕ඞཁͱͳΔɽಛʹɼϊʔυ͕૿. ΔɽཧऀϊʔυɼࢀՃऀϊʔυ͔ΒૹΒΕ͖ͯͨஅย. Ճͨ͠ͱ͖ɼ௨৴େ͖͘૿Ճ͢Δɽ͜Εൿີࢄॲ. σʔλͷ߹ܭͱࣗͷൿີσʔλΛͯ͢Ճࢉ͢Δ͜ͱ. ཧͰ֤ϊʔυ͕ࣗΑΓ ID ͷߴ͍ͯ͢ͷϊʔυʹஅย. Ͱɼͯ͢ͷϊʔυͷαϙʔτͷ߹ܭΛ͠ࢉܭɼසग़Ξ. σʔλΛૹ৴͍ͯ͠Δ͜ͱ͕ݪҼͰ͋Δɽ. ΠςϜηοτͷࢉܭΛߦ͏ɽͦͷޙɼසग़ΞΠςϜηοτ. ຊఏҊख๏ɼൿີࢄ๏Λ༻͍ͨࢄσʔλϚΠχϯ. ͔Βɼ࣍ͷසग़ΞΠςϜηοτͷީิΛੜ͢Δɽ্هͷ. άʹࢀՃ͢Δϊʔυͷͷ૿ՃʹΑͬͯɼաͳ݁ୗੑ. ॲཧΛ͞ 1 ͷΞΠςϜηοτީิ͔Β܁Γฦ࣮͠ߦ͢Δɽ. Λఏʹͱ͜͏·ͯ͠͠ڙ͢ΔɽϢʔβʹඞཁͳ݁ୗ. ྫͱͯ͠ɼҩྍؔػͷσʔλϚΠχϯάʹͯΊͨॲ. ੑΛࣄલʹࢦఆͯ͠Β͍ɼͦͷཁ߹ʹٻக͢Δ݁ୗ. ཧͷྲྀΕΛड़Δɽ͡Ίʹɼ͞ 1 ͷΞΠςϜηοτͰ. ੑΛఏ͢ڙΔΑ͏ʹ CRDM Λมߋ͢Δ͜ͱͰɼϓϥΠό. ͋Δ { ಄௧ }ɼ{ ͜ݞΓ }ɼ{ ෩अ } ͷαϙʔτΛ֤ϊʔ. γʔอͨ͠ޢࢄσʔλϚΠχϯάͷ௨৴ίετΛ͢ݮ. υͰ͢ࢉܭΔɽͦͷޙɼൿີࢄ๏Λ༻͍֤ͯϊʔυͷ֤. Δɽख๏ͱͯ͠ɼ͡Ίʹઅ 3.4 ʹड़Δ௨৴૬खܾఆΞ. ΞΠςϜηοτͷαϙʔτΛཧऀϊʔυͰू͢ܭΔɽ. ϧΰϦζϜΛ༻͍ͯɼࣄલʹϢʔβͷཁ͢ٻΔ݁ୗੑ. ूܭͷ݁Ռͱͯ͠ɼmin sup Λӽ͑ΔαϙʔτΛ࣋ͭΞ. Λຬͨ͢Α͏ʹɼൿີࢄ๏Ͱ௨৴͢Δ૬खΛܾఆ͢Δɽ. ΠςϜηοτΛසग़ͷΞΠςϜηοτͱͯ͠நग़͢Δɽ࣍. ͦͷޙɼमਖ਼ޙͷ CRDM Λ༻͍ͯࢄσʔλϚΠχϯά. ʹ͞ 1 ͷසग़ΞΠςϜηοτ͔Β͞ 2 ͷසग़ΞΠςϜ. Λߦ͏ɽ͜ͷΑ͏ʹ͢Δ͜ͱͰɼ௨৴ίετͷݮΛਤΔɽ. ηοτͷީิΛੜ͢Δɽྫͱͯ͠ɼ͞ 1 ͷසग़ΞΠς Ϝηοτͷ { ಄௧ }ɼ{ ͜ݞΓ }ɼ{ ෩अ } ͔Βɼ{ ಄௧ɼ. 3.4 ௨৴૬खܾఆΞϧΰϦζϜ. ͜ݞΓ } { ಄௧ɼ෩अ }ɼ{ ͜ݞΓɼ෩अ } ͕ੜ͞Ε. ఏҊख๏ͰɼϢʔβʹࣄલʹ݁ୗੑ R (1 ≤ R ≤. Δɽͦͷޙɼ͍ͯ͠ࢉܭࡏݱΔසग़ΞΠςϜηοτީิ. M − 2) Λࢦఆͯ͠Β͍ɼൿີࢄ๏ʹൃੜ͢Δ֤ϊʔ. ⓒ 2016 Information Processing Society of Japan. 3.
(4) Vol.2016-DPS-168 No.4 Vol.2016-SPT-21 No.4 Vol.2016-EIP-74 No.4 2016/11/17. ใॲཧֶձڀݚใࠂ IPSJ SIG Technical Report. υͷૹ৴ͱड৴ͷ߹ܭΛ R ʹ੍͢ݶΔ͜ͱʹΑͬͯɼ ϢʔβͷϓϥΠόγʔอʹޢର͢ΔཁٻΛຬͨͭͭ͠σʔ λϚΠχϯάΛߦ͏ɽ ۩ମతʹɼൿີࢄॲཧΛߦ͏લʹɼ֤ϊʔυ͕அย σʔλΛΓͱΓ͢ΔϊʔυΛܾఆ͢Δ͜ͱͰɼ݁ୗੑ ͷௐΛߦ͏ɽ͜͜ͰɼࣗͷஅยσʔλΛ͢ଞϊʔ υͷϦετΛૹ৴ઌϦετɼଞϊʔυ͔ΒஅยσʔλΛड ͚औΔඞཁ͕͋ΔଞϊʔυͷϦετΛड৴ઌϦετͱݺ ͿɽຊఏҊख๏Ͱɼࣄલʹਤ 4 ʹࣔ͢ΞϧΰϦζϜΛ༻ ͍ͯɼૹ৴ઌϦετͱड৴ઌϦετΛੜ͢Δɽͳ͓ɼຊ. ਤ 5 N5 ͷૹ৴ઌϦετͱड৴ઌϦετͷੜͷྲྀΕ. ΞϧΰϦζϜ֤ࢀՃऀϊʔυͰ࣮ߦ͠ɼͯ͢ͷࢀՃ ऀϊʔυͷૹ৴ઌϦετͱड৴ઌϦετΛੜ͢Δɽͦͷ ޙɼੜ͞Εͨࣗͷϊʔυͷૹ৴ઌϦετͱड৴ઌϦε τΛൿີࢄ๏ʹ༻͍Δɽ Input. Ϣʔβͷཁ͢ٻΔ݁ୗੑ R Output. ૹ৴ઌϦετͱड৴ઌϦετ Step.1 ֤ࢀՃऀϊʔυ Ni (1 ≤ i < M ) ͷͨΊͷૹ৴ઌϦετ ͱड৴ઌϦετΛॳظԽ͢ΔͨΊʹɼi ΑΓ ID ͕ߴ͍ͯ͢ ͷࢀՃऀϊʔυΛ Ni ͷૹ৴ઌϦετʹՃ͢Δɽ·ͨɼi ΑΓ ID ͕͍ͯ͢ͷࢀՃऀϊʔυΛ Ni ͷड৴ઌϦετʹ Ճ͢Δɽ Step.2 ID ͷߴ͍ࢀՃऀϊʔυ Ni ͷϦετ͔ΒॱʹɼNi ͷૹ৴ઌ ϊʔυͷͱड৴ઌϊʔυͷͷ߹ ͕ܭR ʹͳΔ·Ͱ Step.2.1 ͱ Step.2.2 Λߦ͏ɽ Step.2.1 Ni ΑΓ ID ͷ͍ࢀՃऀϊʔυͷதͰɼૹ৴ઌϊʔυ ͷͱड৴ઌϊʔυͷͷ߹࠷͕ܭେͰ͋ΔࢀՃऀϊʔυͷ ID Λͯ͢ٻΊΔɽ Step.2.2 Step.2.1 ͰٻΊͨϊʔυͷதͰɼID ͕͍ϊʔυ (Nj ) Λ༏ઌͯ͠બ͠ɼNi ͷड৴ઌϦετ͔Β Nj Λআ͢Δɽ· ͨɼNj ͷૹ৴ઌϦετ͔Β Ni Λআ͢Δɽ͜ͷ 2 ͭͷॲཧɼ Nj ͷૹड৴ઌͷϊʔυͷͷ߹ ͕ܭR Ͱ͋Ε࣮ߦ͠ͳ͍ɽ ਤ 4. ૹ৴ઌϦετͱड৴ઌϦετͷੜΞϧΰϦζϜ. ਤ 6 N4 ͷૹ৴ઌϦετͱड৴ઌϦετͷੜͷྲྀΕ. ʹࣔ͢ N4 ʹର͢ΔॲཧͰɼ͡Ίʹ௨৴͕ଟ͍ N3 ༏ઌͯ͠௨৴ઌ͔Βআ֎͢Δɽͦͷޙɼ͞Βʹ௨৴Λ ͢ݮΔͨΊʹɼID ͷ͍ N1 Λ௨৴ઌ͔Βআ֎͢Δɽ͜ͷ ํʹΑΓɼN1 ͷΈ௨৴͕ଟ͘ͳΔͱ͍ͬͨ௨৴ͷภΓ Λ͗ɼ༨ͳ௨৴Λݮগͤ͞Δɽ ਤ 4 ʹࣔ͢ૹ৴ઌϦετͱड৴ઌϦετͷੜΞϧΰϦ ζϜΛ༻͍ͯੜͨ͠ૹ৴Ϧετͱड৴ϦετͷྫΛɼද. 1 ʹࣔ͢ɽ͜ͷྫͰɼϊʔυΛ 6ɼϢʔβͷཁ͢ٻΔ Step.1 ͰɼཧऀϊʔυҎ֎ͷࢀՃऀϊʔυΛ௨৴૬. ݁ୗੑ R Λ 2 ͱͨ͠߹ͷϦετΛͦΕͧΕ͍ࣔͯ͠. खͱͯ͠બͤ͞Δɽͨͩ͠ɼϊʔυಉ͕࢜ࣗͷஅย. ΔɽදΑΓɼ֤ϊʔυͷ௨৴͕ࢦఆ͞Εͨ݁ୗੑ R. σʔλΛ͋͠͏ͱϓϥΠόγʔอޢੑೳ͕Լ͕ͬͯ͠·. ͷͰ͋Δ 2 ʹͳ͍ͬͯΔ͜ͱ͕Θ͔Δɽ. ͏ɽ͜ΕΛ͙ͨΊʹɼID ͷ͍ϊʔυ͕ ID ͷߴ͍ϊʔ. ද 1 ੜ͞ΕΔૹ৴Ϧετͱड৴Ϧετͷྫ. υஅยσʔλΛ͢ϧʔϧΛઃ͚ΔɽͦͷͨΊɼ֤ࢀՃ ऀϊʔυ Ni ʹରͯ͠ɼi ΑΓ ID ͷߴ͍ϊʔυΛ Ni ͷૹ ৴ઌϦετʹՃ͠ɼi ΑΓ ID ͷ͍ϊʔυΛ Ni ͷड৴ ઌϦετʹՃ͢Δɽ. Step.2 ͰɼΑΓଟ͘ͷ௨৴Λඞཁͱ͢ΔࢀՃऀϊʔυ Λ༏ઌతʹ௨৴૬ख͔Βআ֎͢Δ͜ͱͰɼ֤ࢀՃऀϊʔυ ͷ௨৴Λ R ௐ͢ΔॲཧΛߦ͏ɽ֤ϊʔυͷ௨৴͕. ্هͷΞϧΰϦζϜΛ༻͍ͨޙɼੜͨ͠ૹ৴ઌϦετ. R ҎԼʹͳΔ͜ͱΛ͙ͨΊʹɼૹड৴ઌͷϊʔυͷͷ. ͱड৴ઌϦετΛ༻͍ͯɼ֤ϊʔυ͕ͨ͠ࢉܭαϙʔτ. ߹ ͕ܭR ͷϊʔυʹରͯ͠ॲཧΛߦΘͳ͍ϧʔϧΛઃ͚. ͷूܭΛߦ͏ɽूॲܭཧΞϧΰϦζϜΛਤ 7 ʹࣔ͢ɽ. ΔɽStep.2 ͷॲཧͷྲྀΕΛਤ 5 ͱਤ 6 ʹࣔ͢ɽ͜͜Ͱɼ ֤ϊʔυͷ௨৴Λ 2 ʹ੍͢ݶΔ͜ͱΛඪͱ͢Δɽ ਤ 5 ʹࣔ͢ N5 ʹର͢ΔॲཧͰɼଞͷͯ͢ͷϊʔυ. ूॲܭཧΞϧΰϦζϜΛ༻͍ͨॲཧͷྲྀΕͷྫΛਤ 8 ͱ ਤ 9 ʹࣔ͢ɽ͜ͷྫͰɼϊʔυ̒ɼϢʔβͷཁ͢ٻ Δ݁ୗੑΛ̎ͱͨ͠ɽ. ͷ௨৴͕ 4 ͳͷͰɼID ͷ͍ N1 ͱ N2 ͷ 2 ͭΛ௨৴ઌ. ਤ 8 Ͱɼׂͨ͠அยσʔλ Vi,k,j Λଞͷϊʔυ. ͔Βআ֎͢Δ͜ͱͰɼࣗͷ௨৴Λ 2 ʹ੍͢ݶΔɽਤ 6. ૹ৴͢ΔൿີࢄॲཧͷྲྀΕΛ͍ࣔͯ͠Δɽ֤ϊʔυɼ. ⓒ 2016 Information Processing Society of Japan. 4.
(5) Vol.2016-DPS-168 No.4 Vol.2016-SPT-21 No.4 Vol.2016-EIP-74 No.4 2016/11/17. ใॲཧֶձڀݚใࠂ IPSJ SIG Technical Report. Input. ֤ϊʔυͷૹ৴ઌϦετͱड৴ઌϦετ Output. ൿີࢄ๏ʹΑͬͯɼͯ͢ͷϊʔυͰڞ௨͢Δ͞ k ͷසग़ΞΠςϜηοτΛநग़͢Δɽ Step.1 ֤ϊʔυɼ͕ࣗͨ͠ࢉܭαϙʔτΛϥϯμϜͳͰ ૹ৴ઌϊʔυͷ +1 ͷͷஅยσʔλʹׂ͢Δɽ Step.2 ֤ϊʔυɼׂͨ͠அยσʔλΛૹ৴ઌϦετͷϊʔυ ʹͦΕͧΕૹ৴͢Δɽ Step.3 ֤ϊʔυɼૹΒΕ͖ͯͨஅยσʔλͱࣗͷͨΊͷஅย σʔλΛͯ͢Ճࢉ͢Δɽ Step.4 ֤ϊʔυɼՃࢉͨ͠Λཧऀϊʔυૹ৴͢Δɽ Step.5 ཧऀϊʔυɼStep.3 Ͱ͞ࢉܭΕͨΛ֤ϊʔυ͔Β ड͚औΓɼࣗͷαϙʔτͱड͚औͬͨͷ߹ܭΛ͢ࢉܭΔɽ ͦͷޙɼͨ͠ࢉܭΛ min sup ͱൺֱͯ͠සग़ΞΠςϜηοτ Λੜ͢Δɽ ਤ 7 ϓϥΠόγʔอͨ͠ޢࢄσʔλϚΠχϯάΞϧΰϦζϜ. 4. ੑೳධՁ ຊઅͰɼఏҊख๏ͷ݁ୗੑͷධՁΛߦ͏ɽ·ͨɼ ࢄσʔλϚΠχϯάʹඞཁͳ௨৴ίετʹ͍ͭͯطଘڀݚ Ͱ͋Δ CRDM ͱൺֱΛߦ͏ɽ. 4.1 ݁ୗੑʹ͍ͭͯ ؔ࿈͋ͰڀݚΔ CRDM ͷ݁ୗੑɼM − 2 ͱͳΔɽ ͜ͷɼ߈ܸϊʔυ߈ܸରͷϊʔυҎ֎ͷͯ͢ͷ ϊʔυͱ݁ୗ͢Δඞཁ͕͋Δ͜ͱΛࣔ͢ɽ͔͠͠ɼϊʔυ ͕૿Ճ͢Δ͜ͱʹΑͬͯաͳ݁ୗੑΛఏ·ͯ͠͠ڙ ͏ɽྫͱͯ͠ɼ100 ͷϊʔυ͕σʔλϚΠχϯάʹࢀՃ ͨ͠߹ɼ࣮ࡍʹ݁ୗੑ 10 ͰेͰ͋Δʹؔ ΘΒͣɼ݁ୗੑ͕ 98 ͷॲͰڥཧΛߦ͏ɽ ఏҊख๏ͷΞϧΰϦζϜΛ༻͍ͨ߹ɼ֤ϊʔυͷ݁ୗ ੑਤ 4 ʹड़ͨΞϧΰϦζϜʹΑͬͯɼׂͨ͠α ϙʔτͷૹ৴ͱड৴ͷΛ R ʹͳΔΑ͏ʹௐ͞ΕΔͨ Ίɼ݁ୗੑϢʔβͷཁ͢ٻΔ݁ୗੑ R ͱͳΔɽ. 4.2 ௨৴ίετͷൺֱ ຊͰڀݚɼ௨৴ίετΛʮൿີࢄॲཧͰൃੜ͢Δࢀ ਤ 8. ఏҊख๏Λ༻͍ͨࢄσʔλϚΠχϯάͷॲཧͷྲྀΕ (ൿີ. Ճऀϊʔυؒͷ௨৴ͷʯͱ͢Δɽ֤ϊʔυɼૹ৴ઌϦ. ࢄॲཧ). ετͱड৴ઌϦετʹ͞هΕͨϊʔυͷ͚ͩ௨৴Λߦ͏ɽ ຊఏҊख๏ͷૹ৴ઌϦετͱड৴ઌϦετͷੜΞϧΰϦ ζϜʹΑͬͯɼ֤ϊʔυͰൃੜ͢Δ௨৴ R ʹ੍ͯ͠ݶ ͍ΔɽͦͷͨΊɼϊʔυΛ M ɼϢʔβͷཁ͢ٻΔ݁ୗ ੑΛ R ͱ͢Δͱɼ֤ϊʔυ͕௨৴͢ΔͰ͋Δ R ͱࢀ Ճऀϊʔυͷ M − 1 Λ͔͚ɼૹड৴ͷॏෳΛআ֎͢Δͨ Ίʹ 2 Ͱׂ͕ͬͨ௨৴ίετͱͳΔɽ ຊఏҊख๏ʹΑͬͯൃੜ͢Δ௨৴ίετͷཧɼ ʢ(M − 1) ʷ. R 2 ʣͱͳΔɽ͜ͷࣜΛ֬ೝ͢ΔͨΊʹɼϊʔ. υͱϢʔβͷཁ͢ٻΔ݁ୗੑΛͦΕͧΕมԽͤ͞ɼ ਤ 9. ఏҊख๏Λ༻͍ͨࢄσʔλϚΠχϯάͷॲཧͷྲྀΕ (ूܭ ॲཧ). ཧͱ࣮ࡍͷ௨৴ίετͷൺֱΛߦͬͨɽൺֱͨ݁͠Ռ Λਤ 10 ʹࣔ͢ɽ͜ͷਤͰɼϢʔβ͕ཁ͢ٻΔ݁ୗੑ RΛ. M −1 2. ͱ 23 (M − 1) ͱ͍ͯ͠Δɽ. ͕ࣗͨ͠ࢉܭαϙʔτ Vi,k ΛϥϯμϜͳͰૹ৴ઌ ϊʔυͷ +1 ͷͷஅยσʔλʹׂ͢Δɽׂͨ͠அ ยσʔλΛૹ৴ઌϦετʹࣔ͞Εͨϊʔυૹ৴͢Δɽ ਤ 9 ͰɼࢀՃऀϊʔυ Ni ड৴ઌϦετʹࣔ͞Εͨ ϊʔυ͔Βड৴ͨ͠அยσʔλͱࣗͷͨΊͷஅยσʔλ. Vi,k,1 Λͯ͢Ճࢉ͠ɼཧऀϊʔυૹ৴͢ΔྲྀΕΛࣔ ͍ͯ͠Δɽཧऀϊʔυͯ͢ͷϊʔυ͔Βஅยσʔλ ͷ߹ܭΛड͚औΓɼࣗͷαϙʔτͱՃࢉ͢Δ͜ͱͰ ͯ͢ͷϊʔυͷαϙʔτΛू͢ܭΔ͜ͱ͕ग़དྷΔɽ Ҏ্ͷॲཧʹΑΓɼ֤ϊʔυͷαϙʔτΛൿಗ͠ͳ͕ ΒࢄσʔλϚΠχϯάͷूॲܭཧΛߦ͏͜ͱ͕Ͱ͖Δɽ ·ɼ֤ϊʔυͷ௨৴Λ R ʹ੍͢ݶΔ͜ͱͰɼগͳ͍௨৴ ͰूॲܭཧΛߦ͏͜ͱ͕Ͱ͖Δɽ. ⓒ 2016 Information Processing Society of Japan. ਤ 10. ௨৴ίετͷཧͱ࣮ࡍͷͷൺֱ. 5.
(6) Vol.2016-DPS-168 No.4 Vol.2016-SPT-21 No.4 Vol.2016-EIP-74 No.4 2016/11/17. ใॲཧֶձڀݚใࠂ IPSJ SIG Technical Report. ਤ 10 ΑΓɼཧͱ࣮ࡍͷ௨৴ίετ΄΅Ұகͯ͠ ͓ΓɼຊఏҊख๏Ͱൃੜ͢Δ௨৴ίετʢ(M − 1) ʷ. R 2ʣ. ্هͷཧΛ༻͍ͨ௨৴ίετʹ͓͚ΔఏҊख๏ͱ. CRDM ͷൺֱ݁ՌΛਤ 11 ʹࣔ͢ɽఏҊख๏ͰɼϢʔβ ͕ཁ͢ٻΔ݁ୗੑ R Λ. ͱ. ຊఏҊख๏Λ༻͍Δ͜ͱʹΑͬͯɼ֤ϊʔυࢦఆͨ͠ ݁ୗੑ R Λຬͨͨ͠ͰڥࢄσʔλϚΠχϯάΛߦ. Ͱಋ͘͜ͱ͕Ͱ͖Δ͜ͱ͕Θ͔Δɽ. M −1 2. 4.4 ߟ. 2 3 (M. − 1) ͱͨ͠߹. ͏͜ͱ͕Ͱ͖Δɽ ͞Βʹਤ 11 ΑΓɼຊఏҊख๏Λ༻͍Δ͜ͱʹΑͬͯɼR Ͱׂࣔͨ͠߹·Ͱ௨৴ίετΛ͕ͱ͖ͨ͜ͰݮΘ͔Δɽ ·ͨɼϊʔυ͕૿Ճ͢ΔʹͭΕͯɼCRDM ΑΓ௨৴ί. ͷ݁ՌΛൺֱʹ༻͍ͨɽ. ετΛେ͖͖͘ͰݮΔɽ͜ΕʹΑΓɼωοτϫʔΫଳҬ ͷѹഭʹΑΔ௨৴࣌ؒͷ૿ՃΛ͑Δ͜ͱ͕ظͰ͖Δɽ ͔͠͠ɼ࣮͍͓ͯʹڥݧɼCRDM ͱఏҊख๏ͷॲཧ ࣌ؒͷେ͖ͳมԽݟΒΕͳ͔ͬͨɽ͜Εɼ୯Ұͷ PC Ͱར༻ڥΛ࠶ͨͨ͠ݱΊʹɼωοτϫʔΫଳҬ͕ेʹ ͘ɼଳҬͷѹഭʹΑΔॲཧ࣌ؒͷ૿Ճ͕ൃੜ͠ͳ͔ͬͨ ͜ͱ͕ߟ͑ΒΕΔɽࠓޙɼଳҬͷॲͰڥ͍ڱཧ࣌ؒͷ มԽΛܭଌ͠ɼ࠶ධՁΛߦ͏͜ͱͰຊڀݚͷ༏ҐੑΛ֬ೝ ͢Δɽ. 5. ·ͱΊ ਤ 11. ௨৴ίετͷൺֱ. ຊจɼҩྍؔػͷΑ͏ͳෳҬʹ·͕ͨͬͯࡏ ͍ͯ͠ΔҩӃͷσʔλϕʔεΛରͱͨ͠ࢄܕ૬ؔϧʔ ϧϚΠχϯάͷϓϥΠόγʔอͱޢ௨৴ίετͷݮख๏. 4.3 ॲཧ࣌ؒͷൺֱ. ΛఏҊͨ͠ɽར༻ऀͷཁ͢ٻΔ݁ୗੑʹ߹Θͤͯϓϥ. ͜͜Ͱɼॲཧ࣌ؒʹ͓͚ΔఏҊख๏ͱ CRDM Λൺֱ. Πόγʔอॲޢཧʹඞཁͳ௨৴Λ੍͢ޚΔ͜ͱͰɼ௨৴ί. ͨ݁͠ՌΛࣔ͢ɽσʔλϚΠχϯάʹ༻͍࣮ͨڥݧΛද. ετͷݮΛਤͬͨɽੑೳධՁͰɼཁ͢ٻΔ݁ୗੑ. 2 ʹࣔ͢ɽ·ͨɼࢄσʔλϚΠχϯάʹ༻͍ͨύϥϝʔ. Λຬͨ͢ͰڥσʔλϚΠχϯά͕Ͱ͖Δ͜ͱΛࣔͨ͠ɽ. λΛද 3 ʹࣔ͢ɽද 2 ͱද 3 ʹࣔͨ͠ॲͰڥཧ࣌ؒΛ. ͞Βʹɼ௨৴ίετʹ͍ͭͯؔ࿈͋ͰڀݚΔ CRDM ͱൺ. ൺֱͨ݁͠ՌΛද 4 ʹࣔ͢ɽ. ֱΛߦ͍ɼগͳ͍௨৴ͰσʔλϚΠχϯά͕ߦ͑Δ͜ͱ. ද 2 ࣮ڥݧ. Λࣔͨ͠ɽ͜ΕʹΑΓɼωοτϫʔΫଳҬͷѹഭʹΑΔ௨ ৴࣌ؒͷ૿ՃΛ͑Δ͜ͱ͕ظͰ͖ΔɽࠓޙɼଳҬͷ ॲͰڥ͍ڱཧ࣌ؒͷมԽΛܭଌ͠ɼ࠶ධՁΛߦ͏͜ͱͰ ॲཧ࣌ؒʹ͓͚Δຊڀݚͷ༏ҐੑΛ֬ೝ͢Δɽ ࢀߟจݙ [1]. ද 3. ࢄσʔλϚΠχϯάʹ༻͍ͨύϥϝʔλ. [2]. [3]. [4] ද 4. ॲཧ࣌ؒͷൺֱ. [5]. ⓒ 2016 Information Processing Society of Japan. S. Urabe, J. Wang, E. Kodama, T. Takata : A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining, IPSJ Transactions on Databases, Vol. 48, No. SIG 11, pp.104–117, 2007. D.W. Chung, J. Han, V.T. Ng, A.W. Fu, and Y. Fu : Fast Distributed Algorithm for Mining Association Rules, Proc. International Conference on Parallel and Distributed Information Systems, pp.31–42, 1996. R. Agrawal, R. Srikant : Fast Algorithms for Mining Association Rules in Large Databases, Proc. 20th International Conference on Very Large Data Bases, pp.487– 499, 1994. M. Kantarcioglu and C. Clifton : Privacy-preserving Distributed Mining of Association Rules on Horizontally Partitioned Data, IEEE Transaction on Knowledge and Data Engineering, Vol.16, No.9, pp.1026–1037, 2004. Goldreich, O: Secure multi-party computation (working draft), Available from <http://www.wisdom.weizmann. ac.il/˜oded/pp.html>(Sept. 1998).. 6.
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