quantifying target entity’s present and expected absolute trustworthiness yang li, h. anthony...
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3 Introduction 1/2 This paper develops a computing method to calculate an entity’s present trustworthiness expected trustworthiness The entity may be any socially behaving element including a person, a terminal, an IPv6 address, a technology, software, or a network Trustworthiness good, honest, and sincere This paper consider entity’s history and environment factorsTRANSCRIPT
Quantifying Target Quantifying Target Entity’sEntity’s
Present and Expected Present and Expected Absolute Absolute
TrustworthinessTrustworthiness
Yang Li, H. Anthony Chan, and George KalebailaDepartment of Electrical EngineeringUniversity of Cape TownNovember 14, 2006
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OutlineOutline IntroductionIntroduction Other Trustworthiness ResearchOther Trustworthiness Research Computing TrustworthinessComputing Trustworthiness Use Case of Trustworthiness ComputationUse Case of Trustworthiness Computation Conclusions and Future WorkConclusions and Future Work
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Introduction 1/2Introduction 1/2 This paper develops a computing method to calculate
an entity’s present trustworthiness expected trustworthiness
The entity may be any socially behaving element including a person, a terminal, an IPv6 address, a technology, software, or a network
Trustworthiness good, honest, and sincere This paper consider entity’s history and environment
factors
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IntroductionIntroduction 2/22/2
Trust Relation
Services
Money
Internet
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OutlineOutline IntroductionIntroduction Other Trustworthiness ResearchOther Trustworthiness Research Computing TrustworthinessComputing Trustworthiness Use Case of Trustworthiness ComputationUse Case of Trustworthiness Computation Conclusions and Future WorkConclusions and Future Work
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OtherOther TrustworthinessTrustworthiness ResearchResearch Most current research into trust or trustworthiness is
for E-commerce Trustworthiness research methodologies
Analyze the generic concept of trustworthiness Potential gain, risk, trust in other party, and trust in control
mechanisms Reinforce the basic concept of trustworthiness by
considering as many causal factors of trustworthiness as possible
Formulize the trustworthiness Use modeling language
Presented a pictorial language to model the dynamic nature of trust
Simulate trust or trustworthiness in software
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OutlineOutline IntroductionIntroduction Other Trustworthiness ResearchOther Trustworthiness Research Computing TrustworthinessComputing Trustworthiness Use Case of Trustworthiness ComputationUse Case of Trustworthiness Computation Conclusions and Future WorkConclusions and Future Work
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ComputingComputing TrustworthinessTrustworthiness Present Trustworthiness
Historical factor: TRt-() Environment factor: Yt,x()
with time t and environment x The reliance of present trustworthiness on the entity’s hist
ory factor and environment factor
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PresentPresent TrustworthinessTrustworthiness Authors use a cos( ) function to simulate an environment facto
r Yx() where Bell’s initial trustworthiness is “0”, we can then depict the trustworthiness on the environment factor in Fig. 1
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CoefficientCoefficient FactorsFactors ofof PresentPresent TrustworthinessTrustworthiness
Authors first compare two individual types of trustworthiness value series when α=0.9 and α=0.7 in Fig. 2 to see how the reliance factor affects the trustworthiness computation:
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SamplingSampling RateRate The sampling rate also influences the accuracy of th
e delay between the trustworthiness value and the environment factor and the closeness of them, as shown in Fig. 3
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ExpectedExpected TrustworthinessTrustworthiness 1/31/3
We express the increase of the environment factor at moment t as ΔYt and define ΔYt=Y(t+1)-Y(t)
we assume that the expected increase of the Y() function at present moment, ΔYt’ , is directly proportional to the increase of Y() function at previous moment t-1
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ExpectedExpected TrustworthinessTrustworthiness 2/32/3
δYδY is a constant, for filtering the burrs of the expected Y value
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ExpectedExpected TrustworthinessTrustworthiness 3/33/3
We randomly compose the following functions to respectively simulate Continuous factor Ycont(t) Discrete factor Ydisc(t) Combined factor Ycomb(t)
Unit Step Function U(t)=1 if t>=0, 0 otherwise
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ExpectedExpected TrustworthinessTrustworthiness onon ContinuousContinuous FactorFactor
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ExpectedExpected TrustworthinessTrustworthiness onon DiscreteDiscrete FactorFactor
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ExpectedExpected TrustworthinessTrustworthiness onon CombinedCombined FactorFactor
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Effect of “Effect of “δδ ” on Expected Trustworth ” on Expected Trustworthiness of iness of Continuous FactorContinuous Factor
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Effect of “Effect of “δδ ” on Expected Trustw ” on Expected Trustworthiness of orthiness of Discrete FactorDiscrete Factor
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Effect of “Effect of “δδ ” on Expected Trustw ” on Expected Trustworthinessorthiness Authors suggest using a larger δY value
Little negative effect on the continuous part (Fig. 7)
Great benefit to the discrete part (Fig. 8)
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OutlineOutline IntroductionIntroduction Other Trustworthiness ResearchOther Trustworthiness Research Computing TrustworthinessComputing Trustworthiness Use Case of Trustworthiness ComputationUse Case of Trustworthiness Computation Conclusions and Future WorkConclusions and Future Work
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Use Case of Trustworthiness Use Case of Trustworthiness ComputationComputation
The HIFGN (Human Intelligence Future Generation Network) has six possible policies of service delivery to choose from according to the intelligence mechanism:
1. Successfully deliver the service to Bell - Success2. Deliver service with less satisfactory performance - Force3. Postpone the service and wait till Bell is available - Wait4. Find Coal to assist Bell with the service – Help5. Bell learn from Coal on how to process the service - Learn6. Fail the service - Fail
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Computing TrustworthinessComputing Trustworthiness Authors proposed [16] a trust scheme to calculate Bel
l’s trustworthiness using four parameters: one history factor and three environment factors
History factor: TRt-(t) The three environment factors are:
Trust on Neighbor: TRn(t) Trust on Domain: TRd(t) Trust on Self-recommendation: TRs(t)
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Trust on TimeTrust on Time
0.9*0.6=0.54
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Trust on NeighborTrust on Neighbor
0.6*0.3=0.18
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Trust on DomainTrust on Domain
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Trust on Self-Trust on Self-recommendationrecommendation
S is the trustworthiness value of Trust on Domain when r=D (D is threshold)
TRs=[S+[(1-S)/(1-D)]*(r-D)=0.6+[(1-0.6)/(1-0.4)]*(0.7-0.4)=0.8When D=0.4 and S=0.6
?
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Total TrustworthinessTotal Trustworthiness
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Application of Application of Trustworthiness on HIFGNTrustworthiness on HIFGN
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OutlineOutline IntroductionIntroduction Other Trustworthiness ResearchOther Trustworthiness Research Computing TrustworthinessComputing Trustworthiness Use Case of Trustworthiness ComputationUse Case of Trustworthiness Computation Conclusions and Future WorkConclusions and Future Work
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Conclusions and Future Work Conclusions and Future Work 1/21/2
Authors proposed a computation method to quantify the present and expected trustworthiness values of a social entity
The paper focused on computing the trustworthiness of a single entity
The computation can only propagate the expected trustworthiness to a moment one short step ahead
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Conclusions and Future Work Conclusions and Future Work 2/22/2
Authors further suggest the following aspects of computation to perfect the computation method of trustworthiness Consider the error between the real trustworthiness at
moment t and the expected trustworthiness at moment t Increase the accuracy of expectation
Use fuzzy logic or cloud theory to more accurately simulate large quantity of dynamic and uncertain input parameters for trustworthiness calculation
Filter most unlikely trustworthiness values to remove the malicious assessment of trustworthiness to a certain degree (e.g., malicious assessment from neighbors)
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ErrorError Page 2 line 2 Page 2 line 2 (left column) : [2] : [2] [3] [3] Page 4 line 14 and 20 Page 4 line 14 and 20 (left column) : eq.(4) : eq.(4) eq.(3) eq.(3) Page 6 line 21(left column) and 14(right column):
constant constant Page 6 Fig. 10.
(curve with square mark) (curve with circle mark)
Average Trustworthiness Page 7 line 3 (left column) : eq.(4) Page 7 line 3 (left column) : eq.(4) eq.(5) eq.(5) Page 7 equation (7):Page 7 equation (7):
[(1-S)/(1-D)]*r S+[(1-S)/(1-D)]*(r-D)
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Q Q && A A
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Fuzzy LogicFuzzy Logic 1965 UC Berkeley 1965 UC Berkeley 教授教授扎德扎德 (L.A.Zadeh(L.A.Zadeh ））發表發表模模糊集合糊集合 (Fuzzy Set)(Fuzzy Set) 論文。論文。
模仿人類思考，處理存在於所有物理系統中的模仿人類思考，處理存在於所有物理系統中的不精確不精確本質本質的數位控制方法學。的數位控制方法學。 模糊理論認為，模糊理論認為，人類的思考邏輯是模糊人類的思考邏輯是模糊的，即使是條的，即使是條件和資料不明確時，仍必須作下判斷。件和資料不明確時，仍必須作下判斷。 模糊邏輯理論卻能提供一種方法，將研究對象以模糊邏輯理論卻能提供一種方法，將研究對象以 00 與與 11之間的數值來表示模糊概念的程度，稱為之間的數值來表示模糊概念的程度，稱為「部分函「部分函數」數」 (membership function)(membership function) 將人類的主觀判斷數值化將人類的主觀判斷數值化，，使得研究結果更能符合人類思考模式。使得研究結果更能符合人類思考模式。 Fuzzy Logic Fuzzy Logic 最先是應用在傳統的控制系統。例如日本最先是應用在傳統的控制系統。例如日本的洗衣機製造廠所推出的洗衣機製造廠所推出 FUZZYFUZZY 智慧型衣機，裝有「智慧型衣機，裝有「 FF
uzzy Logicuzzy Logic 」晶片的洗衣機，有超過百分之七十是用」晶片的洗衣機，有超過百分之七十是用 FFuzzy Logicuzzy Logic 來作控制，這種洗衣機能根據待洗衣物之纖來作控制，這種洗衣機能根據待洗衣物之纖維成分，辨認水中骯髒的程度，進而調整洗衣機的轉維成分，辨認水中骯髒的程度，進而調整洗衣機的轉速、清洗時間、洗衣粉用量以及清洗週期。速、清洗時間、洗衣粉用量以及清洗週期。例如：衣例如：衣服很骯髒，清洗時間即會久些之類的判斷。 服很骯髒，清洗時間即會久些之類的判斷。
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Cloud TheoryCloud Theory Cloud Theory 是以研究是以研究定性定量間的不確定定性定量間的不確定性轉換為基礎的系統性轉換為基礎的系統，處理不確定性問題，處理不確定性問題的一種理論的一種理論
Cloud Theory 為為 Data MiningData Mining 和和 Knowledge DisKnowledge Discoverycovery 中的許多基礎性關鍵問題提供了新的解中的許多基礎性關鍵問題提供了新的解決方法，如概念和知識表達、定性定量轉換決方法，如概念和知識表達、定性定量轉換
它用期望值例如：它用期望值例如： Entropy Entropy (Quantity, (Quantity, 定量定量 )) 表徵表徵 QQualitative(ualitative( 定性定性 )) 概念概念