Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

እቲ SARS-CoV2-ሃገር-ከተት ንሕብረተሰብና ዓበይቲ ብድሆታት ይገብረሎም። ኣብ ዝመጹ ኣዋርሕ ምውጋድ ልበዳታት ዕላማ ክኸውን ኣለዎ ከምኡ'ውን ሽሕ'ኳ ብቀጻሊ ዘሎ ድንገት ልበዳ እናሃለወ ኩሎም ቦታታት ህዝባውን ቍጠባውን ናብራ ብዝተኻእለ መጠን ክዕቀቡ ኣለዎም። ኣደብ ናይ ኩሉ ውልቀ-ሰብ እዩ ዝቑጸር፥ ጽኑዕ ምኽታል ሕጋጋት ርሕቐትን ጽሬትን ጥራይ ንውልቃውን ባዕዳውን ምክልኻል ካብ SARS-CoV-2 ዘገልግል ዘይኮነ፣ እንተላይ ተወሰኽቲ ካልኦት ሕማማት ልበዳ ክዕግት ይኽእል። ብምትሕብባር ሓባር ዝተሰከሙ ስጉምትታት ከከም ኩነታቱ ዝተስተኻኸለ እቲ ምንቅስቓስ ልበዳ ሕብረተሰባውን ቍጠባውን ናብራ ከየቋረጽ እንከሎ ናይ ሰብ መከራን ሞትን መታን ክውገድ ቍጽጽር ክወሃበሉ ይከኣል። እዚኦም ስጉምትታት ኩሉ ግዜ ተቘጻጺሮም ከምኡ'ውን ተስተኻኺሎም።

Organization for Economic Co-Operation and Development (OECD).

Ready or not, the digitalization of information has come, and privacy is standing out there, possibly at stake. Although digital privacy is an identified priority in our society, few systematic, effective methodologies exist that deal with privacy threats thoroughly. This paper presents a comprehensive framework to model privacy threats in software-based systems. First, this work provides a systematic methodology to model privacy-specific threats. Analogous to STRIDE, an information flow–oriented model of the system is leveraged to guide the analysis and to provide broad coverage. The methodology instructs the analyst on what issues should be investigated, and where in the model those issues could emerge. This is achieved by (i) defining a list of privacy threat types and (ii) providing the mappings between threat types and the elements in the system model. Second, this work provides an extensive catalog of privacy-specific threat tree patterns that can be used to detail the threat analysis outlined above. Finally, this work provides the means to map the existing privacy-enhancing technologies (PETs) to the identified privacy threats. Therefore, the selection of sound privacy countermeasures is simplified.

/pdf/a-privacy-threat-analysis-framework-supporting-the-4fc3u4v21j.pdf

A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements

The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real-world, enabling many new services that will improve people's everyday lives, spawn new businesses and make buildings, cities and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality.

Privacy in the Internet of Things: Threats and Challenges

Cloud computing refers to the underlying infrastructure for an emerging model of service provision that has the advantage of reducing cost by sharing computing and storage resources, combined with an on-demand provisioning mechanism relying on a pay-per-use business model. These new features have a direct impact on information technology (IT) budgeting but also affect traditional security, trust and privacy mechanisms. The advantages of cloud computing—its ability to scale rapidly, store data remotely and share services in a dynamic environment—can become disadvantages in maintaining a level of assurance sufficient to sustain confidence in potential customers. Some core traditional mechanisms for addressing privacy (such as model contracts) are no longer flexible or dynamic enough, so new approaches need to be developed to fit this new paradigm. In this chapter, we assess how security, trust and privacy issues occur in the context of cloud computing and discuss ways in which they may be addressed.

https://www.hpl.hp.com/techreports/2012/HPL-2012-80R1.pdf

Privacy, Security and Trust in Cloud Computing

We conduct more and more of our daily interactions over electronic media. The EC-funded project PRIME (Privacy and Identity Management for Europe) envisions that individuals will be able to interact in this information society in a secure and safe way while retaining control of their privacy. The project had set out to prove that existing privacy-enhancing technologies allow for the construction of a user-controlled identity management system that comes surprisingly close to this vision. This paper describes two key elements of the PRIME identity management systems: anonymous credentials and policy languages that fully exploit the advanced functionality offered by anonymous credentials. These two key elements enable the users to carry out transactions, e.g., over the Internet, revealing only the strictly necessary personal information. Apart from presenting for the first time these two key results, this paper also motivates the need for privacy enhancing identity management, gives concrete requirements for such a system and then describes the key principles of the PRIME identity management solution.

/pdf/exploiting-cryptography-for-privacy-enhanced-access-control-3dgv1dszlw.pdf

Exploiting cryptography for privacy-enhanced access control: A result of the PRIME Project

Identity Mixer is an anonymous credential system developed at IBM that allows users for instance to prove that they are over 18 years old without revealing their name or birthdate. This privacy-friendly technology is realized using zero-knowledge proofs. We describe a formal model of Identity Mixer that is well-suited for automated protocol verification tools in the spirit of black-box cryptography models.

/pdf/a-formal-model-of-identity-mixer-27ggclbwtb.pdf

A formal model of identity mixer

A method for providing e-token based access control to virtual world (VW) spaces includes intercepting a request from a first VW member to invite a second VW member to a VW space within a VW network, the request intercepted outside of the VW network. The access controls also include using a secret code associated with the first member to generate an e-token that includes an identifier of the second member and the space. The access controls further include sending the e-token inside the network. In response to a request for access to the space, the access controls include sending the e-token outside of the network for verification. Upon successful verification of the e-token, the access controls include accessing the network and controlling a guard bot inside the network to grant access for the second member to the space. The guard bot adds the second member to an access control list.

Method, system, and computer program product for providing e-token based access control for virtual world spaces

ly, the proof is the composition of the following proofs. SPK{(certs) : certs are valid (ProveCL) (3) ∧ knowledge of committed values (ProveCommitment) ∧ predicates on attributes in certs (ProveRange, etc. . . )} Implicit in (3) is that the attributes in ProveCommitment and ProveRange are the same as those certified in ProveCL. Thus, in addition to proving an attribute m lies in a given range or a given commitment, the verifier is also assured that m appears in a particular certificate. The basic proof structure for an individual certificate proof is similar to a Schnorr signature, the non-interactive version of a common three-move ZK proof. First the prover computes random values of the form t = g, then computes a challenge c = H(. . . ||t), and computes a response of the form s = r − cα, to prove knowledge of α. We will refer to the value(s) in the first step as t-value(s), and the response(s) as s-value(s). The three major steps in (3) will be described as separate protocols. For each certificate involved in the specification, we must prove possession of a CL-signature, using ProveCL. Depending on the specification, we will also need ProveCommitment to prove knowledge of committed attributes and ProveRange to prove hidden attributes lie in some interval. Further sub-protocols, or “provers” can be added as well to extend the features provided by the library (for example, adding k-show credentials). Since all sub-proofs will share a challenge value, they must run in two steps. First, each sub-protocol outputs the t-values which are all included in a hash to form the challenge. Then, given the challenge, the sub-protocols output the s-values. Data Structures The following three data structures are used for bookkeeping in ProveProtocol. They will be populated at the beginning of the proof from S. Revealed attributes Both parties use S to compute the set of attributes which will be revealed during ProveProtocol. The set cert.Arevealed is defined as the indices of attributes in cert which appear in a predicate using the equals relation. The remaining attributes in the certificate, represented as Arevealed remain hidden during the proof. Note that 1 as attribute index is never in Ar since the master secret m1 is stored at attribute base 1 and is never revealed. Certificate Map The certificate map is a table which links attributes to certificates. For an instance certMap, we may call certMap.add(cert, m i) to add the information that the attribute m i belongs to cert. We may also store a random exponent to use in the proof, by calling certMap. setExp(cert, m i, r). 20 Licensed Material. 7.1 Proving Holdership at the Cryptographic Layer 7 PROVING HOLDERSHIP OF A CERTIFICATE Commitment Map The commitment map is a table keeping track of which attributes appear in which commitments. To add an entry to the instance commMap, use commMap.add(commitment, commitmentBaseIndex, m i). This is interpreted as the attribute m i is contained in commitment, at index commitmentBaseIndex. For example, if commitment = gih, the index of m i in commitment is 1. The commitment map also has a method commMap.getAttribute(commitment, commitmentBaseIndex), which returns the attribute associated with the specified base. In our example commMap.getAttribute(commitment, 1) would returnmi and commMap. getAttribute(commitment, 2) would return null (since r is not an attribute). Exponent Relations This is a simple table which maps attributes to random values used in the proof. For an instance expRelations, call expRelations.add(m i, r) to indicate that r is to be the random value used for m i in all predicates of the proof involving m i. The method expRelations.contains(mi) returns true if mi was added, and expRelations.get(mi) returns the corresponding random value. Our presentation of ProveProtocol first describes how the Prover generates the proof, and then how the Verifier performs verification. Recall that the specification each party receives as input differs, the certificates are not known to the Verifier. 7.1.1 Protocol: ProveProtocol Input: m1,S, nonce n1 (from Verifier) Output: non-interactive proof of statement in S: (c, s,Common,T) 0. (Setup) Initialize a certificate map certMap, a commitment map commMap, and a table expRelations. The table expRelations is a temporary variable for Step 0. 0.1 Populate bookkeeping data structures using S 0.1.1 foreach predicate pred in S.getPredicates() if rel is invalid, return ⊥. if mi belongs to a certificate cert ∈ S.getCertificates() certMap.add(cert, mi) if mi is committed, then 21 Licensed Material. 7.1 Proving Holdership at the Cryptographic Layer 7 PROVING HOLDERSHIP OF A CERTIFICATE expRelations.add(mi,0). //this just records the fact that mi will require shared randomness. if mi is committed, then commMap.add(ci, baseIndex, mi). //a reference to ci and the commitment base index are stored in mi. 0.1.2 foreach commitment c ∈ S foreach base index b ∈ c if (b, c) not in commMap then commMap.add(c, b, null) // null indicates that this (b, c) is not used 0.1.3 foreach certificate cert ∈ S foreach attribute mi ∈ cert if certMap(mi, cert) does not exist then certMap.add(mi, cert, null) //null indicates that this mi is not used in the proof. 0.2 Generate random values for hidden attributes 0.2.1 foreach certificate cert ∈ S foreach attribute mi ∈ cert if mi 6∈ cert.Arevealed then choose r ∈R ±{0, 1}m∅H certMap.setExp(cert, i, r) if certMap.get(cert, i) 6= null && expRel.contains(mi) then expRelations.put(mi, r) 0.2.2 foreach commitment c ∈ S foreach base index b of c mi =commMap.getAttribute(c, b) if mi 6= null && expRelations.contains(mi) r =expRelations.get(mi) else choose r ∈R {0, 1}m∅H commMap.setExp(c, b, r) 1. Compute t-values 1.1 Initialized a list r. 1.2 Initialize a list T to store the t-values for each proof, and a list Common to store outputs from sub-protocols which are be common to both the prover and verifier. 22 Licensed Material. 7.1 Proving Holdership at the Cryptographic Layer 7 PROVING HOLDERSHIP OF A CERTIFICATE 1.3 (Call ProveCL.) For each certificate cert ∈ S, do the following. 1.3.1 Let Ar be the set of non-revealed attributes in cert, then set r = (certMap.getExp(cert,mi)mi∈Ar) . 1.3.2 Call ProveCL(Ar, r, cert) and add the output t-value to T, and the output common value to Common. 1.4 (Call ProveCommitment.) For each commitment ci in S, do the following. 1.4.1 Set r = commMap.getExps(ci). 1.4.2 Call ProveCommitment(ci, r) and add the output t-value to T. 1.4.3 Add ci to CO. 1.5 (Call ProveRange.) For each predicate pred in S where pred.rel is valid, do the following. 1.5.1 Let mi be the attribute in pred, and cert be the certificate containing mi. Set r = certMap.getExp(mi). 1.5.2 Call ProveRange(r, pred) and add output t-values to T, common values to Common. We assume the sub-protocols maintain state until the end of the proof (i.e. until both the t-values and s-values have been computed). 2. Compute the challenge c = H(context,CO,Common,T, n1), where • context is a string representing the context (or environment) of the proof, defined in §6.1, • CO is a list of all commitments in S (e.g. values of the form gh in the case of Pedersen commitments), and • Common is a list of common inputs to the zero knowledge proofs. 3. Compute the responses 3.1 Initialize a list s to store the s-values from each proof. 3.2 For each certificate, add the s-values from each proof ProveCL(c) to s. 3.3 Add the s-values from ProveCommitment(c) to s (for the proofs started in Step 1.4). 3.4 Add the s-values from ProveRange(c), to s (for the proofs started in Step 1.5). 4. Output the proof (c, s,Common). Note that version of the Identity Mixer library relies on the order of the list s to determine the association of the response values with the respective prove modules. We now give the details of the three sub protocols. 23 Licensed Material. 7.1 Proving Holdership at the Cryptographic Layer 7 PROVING HOLDERSHIP OF A CERTIFICATE 7.1.2 Protocol: ProveCL ProveCL proves the following, SPK{(e, {mi : i ∈ Ahidden} , v) : ∧ Z ∏ i∈Ak R mi i ≡ ±AS ∏ j∈Ah R mj j (mod n) ∧ mi ∈ {0, 1}m∅H ∀ i ∈ Ah ∧ e− 2`e−1 ∈ {0, 1} ′ e+`∅+`H+2 }(n1) which expands to the following protocol. Protocol:ProveCL Input: Ar, r, cert, [c] Output: if c = null, outputs one t-value, and a common value A′ otherwise outputs s-values Let (A, e, v) be the CL signature for cert. If c is not null, then steps 1-3 have already been executed, skip to Step 4. 1. (randomize signature) 1.1 Choose rA ∈ {0, 1}n∅ 1.2 Compute the blinded CL signature (A′, e, v′), where A′ = ASA (mod n) , v′ = v − erA (in Z) . Additionally compute e′ = e− 2`e−1. 2. (compute t-values) 2.1 Choose random integers re ∈ ±{0, 1} ′ e+`∅+`H rv′ ∈ ±{0, 1}v∅H

Cryptographic Protocols of the Identity Mixer Library

A method comprising, defining a social network U={u, u′, . . . }, defining a set of relationships between users O⊂U×U, defining a set of attributes of the social network A={a1, a2, . . . }, defining a set of contacts of the user u, defining a user profile of the user u Iu=Au∪Uu, defining a metric M={m1, m2, . . . }, receiving a rating of a metric of user u′ from the user u, defining a condition ceC, defining a set of elements Z={z1, z2, . . . }, receiving a request for access to Iu from user u′, determining authorization for user u′ to access a subset of Iu by an access control function U×U×Pow(R)×C→Pow(Iu×Z), and outputting a subset of Iu to user u′ as determined by the access control function.

Access to electronic social networks

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