sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: {0,1}m, de.ne µt to be the input distribution µ, conditioned on the .rst m bits of communication equaling t. Our motivation here is two-fold: .rst, examining general distributions allows us to appeal to the geometry of .({0,1}×[k]).
Sentence 2: we show that the success of a protocol satis.es certain concavity condi­tions when viewed as a function s : .
Relationship: entailment
Domain: Security and privacy
Sentence 1: By doing so, we can not only maintain a smooth continued execution of the sus­pect process after relocation, but ensure its isolation from our monitoring tools.
Sentence 2: fromthesuspectprocess perspective, it isstilllogicallyrunninginside theproduction VM.Inthe meantime,asthesuspectprocessphysicallyruns inside the security VM, the monitoring overhead will not be in.icted to the production VM, thus e.ectively localizing monitoring impact within the security VM.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For example, a resiliency policy in the subcase RCP(s = 0, d = 1)has the form rp(P, 0, 1, t), which asks whether there exists a set of users of size at most t that together have all permissions in P ; while the subcase RCP(t = 8) asks whether there exist several distinct sets of users (d sets) each of whose users together have all permissions in P , even after any set of s users is removed from the state.
Sentence 2: rCP( ) is the general case of the problem.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We study the e.ectiveness of address-space randomization and .nd that its utility on 32-bit architectures is limited by the number of bits available for address randomization.
Sentence 2: we demonstrate a derandomization attack that will convert any standard bu.er-over.ow exploit into an ex­ploit that works against systems protected by address-space randomization.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Since the encryption and decryption keys are unique in our problem domain, we will adopt the succinct notation [x] to represent an encryption or ciphertext of x. an AH-Enc has the following properties: The encryption function is additive homomorphic: [x1 + x2] = [x1] × [x2] mod N2;  Given a constant c and [x], [c · x] = [x]c mod N2;  The encryption function has semantic security as de­.ned in [20], i.e., a set of ciphertexts do not provide additional information about the plaintext to an ad­versary with polynomial-bounded computing power.
Sentence 2: two encryptions of x di.ers with very high probability.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 1 d(Ps(O),Fs(O)) < K~N~ - 1 I P+Q-1I= (1) = ~ (P-=i~---1) recalling that N = PQ is an RSA modulus and ¢ = (P-1)(Q-1) for the second step.
Sentence 2: they are statis- tically indistinguishable for the adversary and the theorem follows.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We can estimate the e.ect of this .lter on the distribution, to the extent that xi models the likelihood that a new person will be able to correctly orient image i.
Sentence 2: the chance that image i will survive the .lter is simply x 10 , so i 10 the resulting distribution would be f'(xi)= xi f(xi).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Backward Traceability: This occursif,given all the internal state of a target tag at time t, the attacker is able to identify target tag interactions that occurred at a time t. <t [15].
Sentence 2: knowledge of a tag s cur­rent internal state could help identify the tag s past interactions, and the past transcripts of a tag may al­low tracking ofthetag owner spastbehaviour[15].
Relationship: entailment
Domain: Security and privacy
Sentence 1: Finally, we take Alice s opinions on her contacts into account by adjusting the value of the likelihood score based on the preference tag pti in ci s pro.le.
Sentence 2: more speci.cally, when pti is green , we may apply a bonus ß (say ß =0.5)to d(ci,f).we have b(pti)=1+ ß.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Contrary tothe belief, subjacent to these papers, that the non-equivocation restriction on a Byzantine adversary improves the degree of replication from n = 3f +1 to n = 2f + 1, we ob­serve that signatures (or transferable authentication) also play an instrumental role along with the non-equivocation restriction in improving the resiliency bound.
Sentence 2: we prove that assuming at most f Byzantine faults in an asynchronous system of n processes, the non-equivocation restriction and transferable-authentication tokens together, but not individually, are su.cient to improve the resiliency bound to n = 2f +1.
Relationship: entailment
Domain: Security and privacy
Sentence 1: One possible explanation of this phenomena against the conclusion in [31] that perturbated precoding is optimal in terms of SINR is that the degrees of freedom in our optimization problem are not full.
Sentence 2: unlike the optimization problem of perturbated precoding matrix which can modify any components in the precoding matrix, our optimization problem can only control the second row of pre­coding matrix, i.e., f21 and f22.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Fi­nally, we evaluate the scalability of CoCA by measuring the latency and CPU usage of CoCA with the respect to parallel forensic data transfer requests (Section 6.3).
Sentence 2: 6.1 Data Acquisition To emulate di.erent and realistic forensic data acquisition scenarios, our .rst set of experiment measures the latency of taking snapshots from the VMs, which run di.erent I­aaS cloud services: computational intensive, memory inten­sive, disk intensive,and networking intensive.the computational intensive VM compute the 1/N!
Relationship: entailment
Domain: Security and privacy
Sentence 1: A policy-speci.c decryption blackbox is a decryption blackbox that is able to decrypt ci­phertexts with some speci.c access policy, say AD.
Sentence 2: unlike a key-like decryption blackbox which has an attribute set, for example, SD attached as the alleged de­cryption capability as the blackbox advertised by the seller, a policy-speci.c decryption blackbox is only known to be able to decrypt ciphertexts with some speci.c policy AD.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Di.erent from a group signature scheme (for examples, [12], [10] and [3]), the group formation is sponta­neous and there is no group manager to revoke the identity of the signer.
Sentence 2: under the assumption that each user is already associated with a public key of some standard sig­nature scheme, a user can form a group by simply collecting the public keys of all the group members including his own.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The requirements on these cross groups {G ' 1 , ..., G ' } are: (1) each m cross group includes exactly one sensor node from each deploy­ment group, and (2) there are no common sensor nodes between any two different cross groups.
Sentence 2: for any iand jwith i j, we have G ' j = i nGj|= 1.
Relationship: entailment
Domain: Security and privacy
Sentence 1: be an (~,c + 1)-coding scheme that follows the multiplicity paradigm with columns ~" c3 i) ln(~) {Vc}.
Sentence 2: then ~ = (iS~WZ log ~ when > K max{(E(V~) log c)/c, clog c} for some constant K. no secure multiplicity code has length e = o( log 5) Proof.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Whereas functional capabilities of technologies are tested via controlled deployments (e.g., pilot projects of smart grids), security evaluations are often not part of the analysis scope in the running pilots.2 Therefore, we think the paradigm of new technologies as social experiments has added value for cyber security.
Sentence 2: when security aspects of the experiments are designed consciously, ethical problems may be avoided, and more data could become available for learning.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We show how an attacker instance can monitor the vic­tim so as to obtain a padding oracle to break XML en­cryption schemes.
Sentence 2: we demonstrate a Ble­ichenbacher attack [6] against SimpleSAMLphp, an open­source SAML-based authentication application that im­plements PKCS#1 v1.5 RSA encryption in a manner re­sistant to these attacks via other vectors (but not via our side-channel attacks).
Relationship: entailment
Domain: Security and privacy
Sentence 1: This has been later generalized to other types of metrics, such as the set di.erence metric [5] and the edit distance metric [6].
Sentence 2: the fuzzy vault [5] uses polynomial interpo­lationinordertoallowauthenticationbasedonthe matching of a su.cient number of features, while the fuzzy extractor [6] is a further generalization which combines the previous constructions with particular objects called random extrac­tors.
Relationship: entailment
Domain: Security and privacy
Sentence 1: and an input identi.er t. Input identi.ers, in isolation, are used to label the variable inputs of a function f, whereas the combination of both, i.e., the full multi-label L = (., t ), is used to uniquely identify a speci.c data item.
Sentence 2: binding a value m with multi­label (., t ) means that m can be assigned to those input variables with input identi.er t. The pair (., t ) is necessary to uniquely identify m. While one can still not re-use a pair (., t )for authentication purposes, one can re-use the input identi.er t, instead.
Relationship: entailment
Domain: Security and privacy
Sentence 1: A migrateable instance is an in­stance that can either be: (i) migrated to the new policy; (ii) migrated to a temporary ad-hoc policy; or (iii) left running according to the old policy without causing violations.
Sentence 2: these are instances we can somehow deal with without revoking privileges or ignoring new restrictions in­troduced by the new policy.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Much of the focus of identity 2.0 (as promoted by organisations such as sxip12) is on the multiplicity of things that we say about ourselves, and the things that others say about us.
Sentence 2: identity 2.0 begins with a realization that the usefulness of online identity depends on context, and it must be responsive to the natures of the diverse relationships we have with those we transact w13 It seems to be increasingly accepted that people live with multiple identities.
Relationship: entailment
Domain: Security and privacy
Sentence 1: ProtectMe architecture is composed by a number of com­ponents, that follows to a certain extent XACML architec­ture for access control, extending it with usage control direc­tives proposed by [4].
Sentence 2: protectMe is composed by: Policy Enforcement Point (PEP), that exposed methods to access protected resources; Policy Decision Point (PDP), that is responsible for evaluating requests received by PEP, interacting with other modules like SPP and PIP; SP man­agement Point (SPP), that mostly handles resource-policy associations, as well as SP retrieval: in fact, it parses SPs  Figure 2: ProtectMe Policy Rendering and stores them in the Android SQLite DB, to optimize per­formances; Policy Information Point (PIP), responsible for gathering data from mobile device sensors; and a fa¸cade, that exposes the publicly available functionalities, in the form of an Android service interface.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2.3 Secure Boot Another application for inherent PUFs is to use it as an identi.er for a given device, which is used to implement a secure boot process tying software to a speci.c physical instantiation of a Cortex-A processor.
Sentence 2: we derive a .ngerprint from the intrinsic SRAM of an System-on-a-Chip (SoC) platform to identify the device.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Recent works on ORAM [21, 24, 25, 30, 31] have shown that even the fastest known constructions incur a large bandwidth overhead in practice.
Sentence 2: under typical parametrizations, and with O(1) amount of client storage, known ORAM construc­tions require 400+ blocks be transferred between a client and a server for a single data access.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Since all malware infections we have observed are trig­gered by users actively downloading malware from the on­line marketplace, detection and removal of malware at the online marketplacehasproved tobeane.ectiveway ofpre­venting fraud activities.
Sentence 2: our analysis suggests that the online marketplace should come up with more ad­vanced vetting process for the apps, especially if it contains code segment involved with voice calls or SMS messages.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Of particular interest are the rules that de.ne how high integrity data is processed securely, where: (1) high integrity data (CDIs) must satisfy integrity veri.­cation procedures (IVPs) (Clark-Wilson rule C1); (2) only approved programs called transformation procedures (TPs) may modify high integrity data (C2, E1); and (3) TPs may only receive low integrity data (UDIs) if that data is up­graded or discarded (C5).
Sentence 2: tPs protect data in­tegrity, but they require IVPs to validate that the data an application depends upon is high integrity and TPs must be capable of mediating low integrity inputs to upgrade or discard such data.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To be conserva­tive, we use the current draw with radio on in our compu­tation.
Sentence 2: we chose 31 mA and 66 mA for Imote2 at  16,018 13,52015,65811,396 16,35217,72812,852 19,308 MICAz Tmote Sky Imote2 20,768 RAM size (byte) ROM size (byte) 25,000 20,000 15,000 10,000 5,000 0 ECDSA ECIES ECDH (a) ROM size Figure 6.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Hence, we aimed to adopt already existing techniques such as shad­ow stack [15, 59, 26] for return addresses, and the concept of binary instrumentation as used in taint tracking [47, 17] or return address protection [29, 16, 37].
Sentence 2: our 2Moreover, it has been used in [61] for a checksum-aware fuzzing tool and in [17] as dynamic taint analysis system.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Then, formula (8) states that predi­cate ipspoofed(sensor, srcI P, srcEth)is trueif and only if sensor can sni.
Sentence 2: packets coming from physical address srcEth (which is connected to link l and is associated with IP address srcEthI P ), and there is no path p be­tween the host srcI P H ost of IP address srcI P and node srcEthI P such that p does not include link l.ipspoofed(sensor, srcI P, srcEth) is true when a packet with source ethernet address srcEth and source IP address srcI P is spoofed, and a sensor on node sensor can detect it.
Relationship: entailment
Domain: Security and privacy
Sentence 1: fraction of a with probability 1 - d. To see this, observe that Q can be used to answer queries a · q for any arbitrary  v q to within o( m) additive error.
Sentence 2: to answer (1) (r) queries a · q1,..., a · qr, run Q(X, S),..., Q(X, S) in tt parallel, where St (i) is a stream with state qi.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The advantages of our approach are: (1) the model is applicable to a wider variety of contexts, and (2) the security requirements are expressed in a clearer way, with bene.ts for the security administrator and the reason­ing system.
Sentence 2: we provide a complete technique for the identi.cation of query compliance with authoriza­tions and formally demonstrate the correct behavior of an e.cient composition of authorizations, whereas in [16] no algorithm is provided for the evaluation of view composi­tion, relying instead on a potentially expensive application of a few heuristics by the query optimizer.
Relationship: entailment
Domain: Security and privacy
Sentence 1: fectiveness as being with respect to utilities whose typicality rank is bounded above by some value r. If, whenever someone commits theft, there is always some non-zero probability that he will be tried, convicted, and sentenced, then because the penalty in question quali.es as punishment this satis.es our working de.nition of account­ability.
Sentence 2: the thief is accountable for his theft (although he might escape punishment in a particular case).
Relationship: entailment
Domain: Security and privacy
Sentence 1: O)k , k denoting the maximum number of parameters in a model s command set.
Sentence 2: the state transition function d of an HRU model is de­.ned by a .nite set of partial de.nitions where each de.nition spec­i.es the parameters, conditions, and effect of a single command in C. In the example above, the condition read_right .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Since the mobility trace does not have an explicit graph structure, supposing the social graph is the ground truth, we apply the technique in [27] on the mobility trace to construct four graphs with di.erent recalls and precisions, denoted by M1, M 2, M 3, and M4,  tp tp respectively (recall = and precision = , where tp+fn tp+fp tp = true positive, fp = false positive, and fn = false neg­ative).
Sentence 2: the recall and precision of M1 are 0.6 and 0.865, of M2 are 0.72 and 0.83, of M3 are 0.75 and 0.78, and of M4 are 0.8 and 0.72, respectively.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2.2 The Ideal System for Group Key Exchange Our ideal system Sysidealgkefor group key exchange models how nservers repeatedly establish a session key.
Sentence 2: we say aserver istarts a session with tag IDwhen an input (start,ID) occurs at ini(IDis an arbitrary bit string and represents a unique identi.er for the session).
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this paper, we mainly investigate the impact of user guidelines on creating click-draw based graphical passwords.
Sentence 2: we conducted two experiments with 60 par­ticipants as follows: (1) Recording created graphical pass­words from 20 participants without user guidelines, and then recording created graphical passwords from the same 20 par­ticipants by providing them with user guidelines; (2) For the remaining 40 participants, we equally divided them into two groups.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Therefore, during design time, we specify t s people link in such a way that the user repository returns all users who are assigned to a role in Rt.
Sentence 2: the user repository keeps track of the user-assignment relation UA and the work.ow de.nition speci.es the permission-assignment relation PA. Implicitly, we assume a one-to-one relation between permis­sions and tasks.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We show how every attribute predicate can be rewritten in terms of building blocks.
Sentence 2: any attribute predicate witharbitrarylogical nesting and negations canbe trans­ formed todisjunctive normalform(DNF),i.e., ij lij , where lij is an atomic expression(AtomicExp in Table 2) with no fur­ther logical structure.
Relationship: entailment
Domain: Security and privacy
Sentence 1: There is a large body of work on improving the security of OSes and mobile apps.
Sentence 2: researchers have paid considerable attention to protecting personal user data (e.g., address book, user photos, password information, GPS location) and preventing its unauthorized access and leakage by proposing novel techniques, such as new access control mechanisms [33] and information .ow analysis [20].
Relationship: entailment
Domain: Security and privacy
Sentence 1: A hybrid hierarchy in which these different hierarchy types coexist can capture fine-grained inheritance semantics [Jos05 Jos02a, Jos03, San98].
Sentence 2: when various separation of duty (SoD) as well as user-centric and permission centric cardinality constraints need to be applied on roles in a hierarchy, A-hierarchy can be used [San98, Jos02a].
Relationship: entailment
Domain: Security and privacy
Sentence 1: The servers will then be able to update the scores for all physicians and all conditions using the data received from the patient without the ability to know which particular value has been modi.ed.
Sentence 2: this can be accomplished as follows: the computational servers maintain (protected) sums rjk and weights wjk for each physician j and condition k.When anew rating rijk of weight wijk is submitted, the sum of ratings is updated as rjk + rijk and the weight is updated as wjk + wijk.When rijk and wijk are both 0, nothing is modi.ed.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Prior work on improving the security con.g­uration in a network of machines has required substantial manual e.ort by the IT administrator [2, 52, 48, 38, 35].
Sentence 2: these systems may repeatedly propose con­.guration changes that are not implementable, or they may require the IT administrator to specify particular high-value machines that must be defended.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The next (sequential) composition property allows to sim­plify cryptographic security proofs and omit unnecessary details.
Sentence 2: let .g|f denote a sequential protocol for comput­ing functionality g, where parties can access a trusted third party TTP that computes functionality f.parties can send their arguments to the incorruptible TTP that privately replies with the answers f1 and f2.Now, let .f|g ..f denote the protocol, where parties execute .g|f but instead of TTP use .f to compute f. Then the following sequential composition theorem [5, p. 637] holds.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 4.1 Computing Aggregates Using a Singly Ho­momorphic Encryption Scheme Consider a homomorphic encryption scheme that is IND-CPA secure with key generation algorithm, encryption algo­rithm and decryption algorithm (Gen, Enc, Dec respectively) and with semigroups of plaintexts and ciphertexts M and C respectively.
Sentence 2: the homomorphic property is such that for m1,m2 .
Relationship: entailment
Domain: Security and privacy
Sentence 1: 5.1 Pacemaker and ICD Reliability Studies The way in which pacemaker and ICD malfunctions are recorded does not allow for the determination of the fre­quency of failure of a particular device model.
Sentence 2: while there have been studies that attempt to measure the relia­bility of these devices, the studies are limited by the fact that malfunctions are only included in the records if the malfunction results in a device replacement.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Though the goal of this paper is not to investigate the existing privacy preserving data mining tasks over the encrypted data, we focus on speci.c underlying secure operations used in these tasks.
Sentence 2: performing secure comparison on the intermediate en­crypted results (i.e., given the encryptions of two integers x and y in the cloud, we need to securely evaluate whether x is greater than or equal to y) is an essential operation in many data analysis tasks such as clustering and classi.cation.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In most practical scenarios, n is signi.cantly larger than k; our protocol is optimized for this case.
Sentence 2: the size of the securely transformed branching program is independent of n. 4.1 Branching programs In this section, we formally de.ne branching programs, which include binary classi.cation or decision trees as a special case.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, these performance metrics are only meaningful with respect to the achievable security level (S ).
Sentence 2: we are interested in how MA sch­emes compare with each other in terms of (i) Ls / S , (ii) Cs / S , and (iii) Cv / S .The lower an MA scheme scores in all these metrics, the better the MA scheme.
Relationship: entailment