sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: Since in certi.cateless cryp­tography, there is no authentication information (such as an X.509 certi.cate in PKI) for the user self-generated public key, an adversary can replace the public key with another one of its choice.
Sentence 2: a Type-II adversary (or AII ) is the KGC who controls the generation of the identity­based user private key but does not know the secret value corresponding to the user self-generated public key.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: In the work [10], the authors propose a multicast protocol to aggregate messages on the basis of XML tree similarity in order to reduce the transmission tra.c in low bandwidth environments.
Sentence 2: while a great e.ort has been made to optimize SOAP performances in transmission, less attention has been given to the problem of reducing the overhead of applying security policy rules to a large number of messages to be transmitted.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: It is worth reporting that, following the approach proposed in [27] for signature recognition, we also performed tests by employ­ing a Bayes Classi.er, Support Vector Machines (SVMs), as well as Principal Component Analysis (PCA) in conjunction with the metrics given in (11)-(14), in order to improve the authentication performances.
Sentence 2: the obtained veri.­cation results do not outperform those obtained by using the MEAN statistics, which produces the best achieved EERs and it is therefore employed hereafter.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: If the doctor rarely asks for more quotas in the past, we may consider that the doctor runs out of quo­tas due to exceptional conditions.
Sentence 2: the system may automatically grant the doctor more quotas so that the doctor can continue with herjob.ifadoctor runsoutof quotas frequently,shemay be over-accessing patients information, as her risk scores must be oftentimes much higher than average.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: In this paper we argue how this attack need not affect protocols that do not rely on IP fragmentation, and argue how most protocols, e.g., those that run on top of TCP, can avoid the need for fragmentation.
Sentence 2: protocols such as IPsec s IKE protocol, which both runs on top of UDP and requires sending large packets, depend on IP packet reassembly.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Unfortu­nately, this source of information carries inherent limitations of the OSN pro.les  they are static (e.g., interests a user declared at the time of joining the OSN) and coarse-grained (e.g., loca­tion information only up to city level).
Sentence 2: with Tessella­tion, the information gained from combining raw traf.c analysis corroborates and compliments the OSN pro.le information and re­veals a wider variety of user activities (i.e., device information, so­cial associations, e-commerce activities, etc.).
Relationship: contrasting
Domain: Security and privacy
Sentence 1: The third and .fth components are provided by several credential technologies, e.g.,IdentityMixer(idemix),U-Prove, orX.509 thatprovide token generation and veri.cation.
Sentence 2: so far no efforts have been madetoprovidethe components(2) and(4) andthereby to close thegapbetweenhigh-level authenticationlanguages andlow-level technology-dependent speci.cation languages responsible for the tokengeneration and veri.cation algorithms.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: THE PROPOSED SCHEME Some DLP-based signatures (e.g., ECDSA [16], Schnorr [22]) can eliminate expensive operations from the signature generation $ by pre-computing and storing the components (R, r), where r .
Sentence 2: r Z* q and R = a mod p. this approach incurs linear stor­age to the signer side (i.e., one token for each message).
Relationship: contrasting
Domain: Security and privacy
Sentence 1: System call forwarding be­tweenVMshasalsobeenused[27] to improve the .delity of runtime environment forbetter malwarebehavior monitor­ing, albeitwithout isolationguarantees.
Sentence 2: we aim toaddresscompatibility of existingprocess monitoring tools while ensuring their strong isolation in the context of VM introspection.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Formalizing GDOI Group Key Management Requirements in NPATRL * Catherine Meadows, Paul Syverson Iliano Cervesato Center for High Assurance Computer Systems Advanced Engineering and Sciences Division Naval Research Laboratory ITT Industries, Inc. Washington, DC 20375-5320, USA Alexandria, VA 22303-1410, USA {meadows, syverson}@itd.nrl.navy.mil iliano@itd.nrl.navy.mil ABSTRACT Although there is a substantial amount of work on formal requirements for two and three-party key distribution pro­tocols, very little has been done on requirements for group protocols.
Sentence 2: since the latter have security require­ments that can di.er in important but subtle ways, we be­lieve that a rigorous expression of these requirements can be useful in determining whether a given protocol can satisfy an application s needs.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Completely separating server activities from client activities eliminates the threats to the bridge oper­ator s privacy, and extends to any other services a user might want to offer on the public Internet, which might also serve as targets of a winnowing attack.
Sentence 2: this also defeats the purpose of the bridge design, which is to create a large, dynamic pool of nodes that must be blocked: if the set of bridges is limited to a static set of nodes, then bridges become nearly as easy to block as relays.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: If an attacker can submit k to the TLD concealed with the correct m, the attacker can recover kd and decrypt the user s sensitive .les.
Sentence 2: we assume an OT is unable to access the constrained channel and thus lacks knowledge of m. Instead, the attacker can try to guess m and interact with the TLD by itself or intercept com­munication between a legitimate user and the TLD to recover the current m. On her own, the attacker has a 2-e chance of guessing the correct m for a given run of the protocol.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Therefore, the intra-chip HD is 0.0% as shown in Fig 7 for both analyses.
Sentence 2: the underlying noise levels can be mea­sured by disabling the thresholding technique, yielding intra-chip HDs of 5.11% and 8.68% for the TGVD and TCD analyses, resp.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: When those color spaces models, the color histogram scheme is the most e.ective way to represent a image since color histograms are invari­ant to orientation and scale [9,31].
Sentence 2: color histogram can hardly characterize the spatial structures of images; as a result, other descriptors have been proposed such as com­pact color moments, the color coherence vector and color correlograms [23].
Relationship: contrasting
Domain: Security and privacy
Sentence 1: MOTIVATION: FINDING VULNERABLE CLIENTS The most common form of proactive defense is getting there .rst, for example, to patch a client to protect against an existing vulnera­bility, or to remove capabilities from the client, making it more se­cure.
Sentence 2: a central problem of proactive defense is to decide which clients are the most vulnerable when a malicious activity is identi.ed in the network, be it an intrusion, a virus or worm.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: 4.2 4 Neighbor Operation Typically there are tampered error blocks and inherent error blocks (due to watermark extraction failure, not tampering).We assume that the tampering is done to a local region spanning a group of blocks and not just an individual block.
Sentence 2: from the watermark extraction in figure 2, we see that inherent error blocks are mostly spatially isolated from each other.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Platoon=X)(Position)) (denoted in what follows as View1).
Sentence 2: sec View(Position,acp2) is given by: Map(Pos, SID)(Filter(Position.Platoon =X.Pos=Target( a)-d.Pos=Target(a)+d.ts=TAction start(a).ts= TAction end(a))(Position)).
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Privacy Aware Access Control for Data Sharing in Cloud Computing Environments Hassan Takabi Department of Computer Science and Engineering University of North Texas Denton, TX, USA takabi@unt.edu ABSTRACT In cloud computing environments, data protection is usu­ally limited to access control policies that are enforced by cloud service providers (CSPs).
Sentence 2: there are many cases where the CSPs are not trusted and pose a risk to their users privacy.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: In all conditions but Chosen Text, passwords were ran­ domly assigned, making the e.ective password space equal Mean SD Median Skewness Kurtosis to the theoretical password space and pre-determining the BPT 15.74 10.48 13.38 3.00 10.19 security of the password system against guessing attacks.
Sentence 2: iPT 12.65 6.14 9.33 1.38 1.52 the password security in Chosen Text was largely OPT 34.61 21.79 24.25 1.74 2.03 determined by participants choice of passwords.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: One option is to let CAs embed the ILS proof in the certi.cate itself, by inserting it into a certi.cate extension.
Sentence 2: once the certi.cate .le is updated with a time-bounded ILS proof, the hash of the updated certi.­cate would not match the original certi.cate recorded in the ILS Integrity Tree.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: This is be­cause an adversary may move the tampered data from one partition that is currently being veri.ed to another partition that is not being veri.ed.
Sentence 2: this attack does not work for MT-SRoT because anytampered datum within the target memory region will be ver­i.ed by one of the computing units at any time (unpredictable to adversary) before the end of veri.cation process.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: 1.3 Scenarios From a security point of view HTTP and HTTPS offer an all-or­nothing choice to web applications; either no security guarantees with HTTP or both con.dentiality and integrity with HTTPS at the price of performance.
Sentence 2: in many scenarios con.dentiality is not necessary and even undesired, while integrity is essential to prevent attackers from compromising the data stream.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Then, for any yi - yj = yj - yk, then an adversary will only succeed with a negligible advantage in distinguish­ing {Txi,Txj,Txk} from {Tyi,Tyj,Tyk}.
Sentence 2: if xi -xj = xj-xk and yi -yj = yj-yk, which means that the relative relationships of distances between the data items are di.erent, then an adversary may succeed with a non-negligible advantage.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: While the motivation for the original work was to improve the accu­racy and automation of an ADS using threshold estimation, the technique can be adapted to estimate the ADS detection threshold for evasion.
Sentence 2: we restrict the evaluation of conditional entropy to .rst order Markov chains only:  H (pj |pi)= -p(pi, pj ) log (p(pj |pi)) .
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Left-handed persons tilt the smartphone slightly to the left side when entering PIN digits in the mid­dle and right column of the key pad, i.e., 2, 3, 5, 6, 8, 9.
Sentence 2: right-handed persons tilt the smartphone slightly to the right side when entering PIN digits in the left and middle column, i.e., 1, 2, 4, 5, 7, 8.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: The diagonal line shows the case of tuple (M = 1,K = 1), in which Pr(X = K)is equal to p. Clearly, if p is smaller than 0.2, all other curves are below this diagonal line, indicating that their values of Pr(X = K) are smaller than that of tuple (M =1,K =1).
Sentence 2: if pis larger than 0.8, these curves are above the diagonal line, indicating that their values of Pr(X = K) are larger than that of tuple (M =1,K =1).
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Still, theseare relatively minor problems.
Sentence 2: a script can gain a refer­ence to an open window with name windowName via var win = window.open("", "windowName") (if no window with thatname exists,anemptywindowwillbecreated).Inour testing,wefound that Opera did not consider frame names when searching for a matching window name, only top-level window names, while In­ternet Explorer and Mozilla Firefox considered both.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Such homomor­phic MAC indeed allows to authenticate degree-2 arithmetic computations over Fq and achieves amortized e.cient ver­i.cation, i.e., after a pre-computation phase whose cost is the same as running f, every output of f can be veri.ed in constant time.
Sentence 2: a straightforward application of the scheme [6] on top of BGV ciphertexts would require to: (1) authenticate each of the 2n Fq-components of a BGV ciphertext, and (2) authenticate the BGV evaluation circuit : F2nt f q .
Relationship: contrasting
Domain: Security and privacy
Sentence 1: If HIFMs are .ow-equivalent, then they have the same data-.ow graphs and hence each of the HIFMs can be represented using any one of the HIFM s data­.ow graphs.
Sentence 2: since each HIFM s input nodes may expect data of di.erent labels, we augment the data-.ow graph with a second layer of dummy input nodes for each unique input label, which we call constraint nodes because they require satisfaction of a label mapping.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: In the best case we were able to recognize 96% of the symbols with 4% errors.
Sentence 2: in the worst case, we are able to detect 44.44% of the key­strokes, although the errors causedby thedif.culty ofdealing with objectsthat coverthescreenpermanently arequitehigh.users seldomhold touchscreendevices withpermanent occlusions, especially while typing on the go.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: The normal client can retrieve the web page in 0.4 sec­onds, but it does not get any response during a background TCP SYN .ood attack due to the effect of control/data plane saturation mentioned earlier.
Sentence 2: aVANT-G UARD can effectively de­fend the network from this attack, enabling the normal client to re­trieve the webpage without any problem, because our data plane au­tomatically and transparently classi.es and removes the malicious TCP connection attempts.
Relationship: contrasting
Domain: Security and privacy
Sentence 1: For each pro­cess, a randomized o.set is added to the starting addresses of stack, heap, library and text segments, making the memory locations of the gadgets harder to predict a priori.
Sentence 2: brute attack [13], partial address overwrites [14] and informa­tion disclosure [15] are potential limitations, to which even some more .ne-grained ASLR schemes [7, 11, 12] have been shown vulnerable [16].
Relationship: contrasting
Domain: Security and privacy
Sentence 1: Our goal is to assess the extent of security issues that may be introduced from vendor customizations and further de­termine how the situation is evolving over time.
Sentence 2: we take a three-stage process: First, given a smartphone s stock im­age, we perform provenance analysis to classify each app in the image into three categories: apps originating from the AOSP, apps customized or written by the vendor, and third-party apps that are simply bundled into the stock image.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Unfortunately, stan­dardprotocols all of which requireasymmetricamount of work are ill-suited for these settings since the size of both parties inputs is bounded by the resources available to the weakest device.
Sentence 2: the mobile device would only be able to search through a small amount of location data, and thePC could only search through a smallfraction of the database.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We tested the two QR code scanners against the attacks using malware obtained from http://malgenomeproject.org [3].
Sentence 2: we used malware under the drive-by download categories (GGTracker, Jifake, Spitmo, and Zitmo) and repackaging malware categories (AnserverBot and DroidKungFu).
Relationship: entailment
Domain: Security and privacy
Sentence 1: 5.2 Bounding edit distance in terms of thenumber of costly literals We wish to show that a high edit distance between a(E) and ß(E) cannot be achieved without a lot of costly literals.
Sentence 2: we wish to construct a method to convert a(E) into ß(E) such that the total number of edit opera­tions (insertions and deletions) performed is bounded by a constant times the number of costly literals.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Second, TrustDroid prevents unauthorized data access, by performing .ne-grained data .ltering on application data and data stored in common databases (SMS, Contacts, etc.).
Sentence 2: this prevents malicious applications from reading data of the corporate domain, as long as the malicious application has not been issued by the enterprise itself, which is excluded in our Adversary Model (cf.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The .rst thing that we can observe from Experiment 2 is that the number of elicited roles is greater than the number of elicited roles in Experiment 1.
Sentence 2: we have 157 roles in the .rst case (that cover 7,857 user-permission as­signments with 2,191 role-user relationships) and 171 roles in the second (that cover 7,857 user-permission assignments with 2,196 role-user relationships).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Beyond just physical security, we also assume that there are strong net­work admission control [14, 7] and anti-virus measures in place on the network in the secure environment.
Sentence 2: an attacker should not be able to access sensitive data by intro­ducing malware into the secure environment, but enforcing this constraint is beyond the scope of Data Tethers.
Relationship: entailment
Domain: Security and privacy
Sentence 1: White space characters cause ambiguity of whether they should match s+ or be counted as part of the 1024 non-return characters [^n]{1024}.
Sentence 2: an input of SEARCH followed by 1024 white spaces and then 1024 a s will have 1024 ways of matching strings, i.e., one white space matches s+ and the rest as part of [^n]{1024}, or two white spaces match s+ and the rest as part of [^n]{1024}, etc.
Relationship: entailment
Domain: Security and privacy
Sentence 1: As a .rst step, we consider a simple algorithm in which a reader ramps up power until it receives a response from a tag.
Sentence 2: our implementation ramps up the reader s power from 15 dBm to 30 dBm (the full range of our reader) in 0.25 dB increments (the minimum supported by our reader), transmitting a KILL command at each power level in turn.
Relationship: entailment
Domain: Security and privacy
Sentence 1: It is easy to see that by using our scheme for univariate polynomials of section 4.2, a client can store encrypted vectors on a server and then request the DFT transform of these vectors in a private and veri.able way.
Sentence 2: note that the delegation and veri.cation cost is optimal: O(t) (i.e., O(1) for every entry of zy).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Due to the rapid evolution and proliferation of the botnets, network based security technologies such as Network based Intru­sion Detection Systems (NIDSs), Intrusion Prevention Sys­tems (IPSs), .rewalls have received remarkable attention to defend our crucial computer systems, networks and sensi­tive information from attackers on the Internet.
Sentence 2: there has been much effort towards high-performance NIDSs based on data mining and machine learning techniques[1, 2].
Relationship: entailment
Domain: Security and privacy
Sentence 1: 4.1 DataSources To buildthe database for our vulnerability analysis, wehave to determine the points in time ofthe vulnerability lifecycle as de.ned in section 3.
Sentence 2: for each vulnerability identi.edwehave to de­termine the correspondingdiscovery, disclosure, exploit andpatch date.As there is no single source to provide this information,the dif.culty ofthis task is to 1. identify suitable sources 2. collectthe information available   3. correlate the information in a concisemanner.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Because each domain must have an SCP (in order to be protected by the PoliCert) and these policies can greatly impact a domain s certi.cate usage, policies can only be updated, not revoked, and must meet several criteria to be accepted by the logs.
Sentence 2: the new policy must be signed with the policy private key of the old policy, as well as by a threshold number of CAs (UP_CA_MIN).
Relationship: entailment
Domain: Security and privacy
Sentence 1: 4.2 Feasibility study To be able to use CSI for user authentication, the mea­sured CSI from di.erent devices should satisfy the unique­ness requirement.
Sentence 2: the CSI measured at di.erent de­vices resided at di.erent locations should be distinct, while the CSI collected from di.erent packets emitted by the same device should be similar, if not identical.
Relationship: entailment
Domain: Security and privacy
Sentence 1: RBAC has been widely used, but has weaknesses: it is labor­intensive and time-consuming to build a model instance, and a pure RBAC system lacks .exibility to e.ciently adapt to changing users, objects, and security policies.
Sentence 2: it is impractical to manually make (and maintain) user to role assignments and role to permission assignments in in­dustrial context characterized by a large number of users and/or security objects.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Note that if an alternative model for permissions is used and a di.erent ordering is de.ned on those permissions, it is still possible and indeed pertinent to insist that Con­straint 2 be satis.ed.
Sentence 2: in the case of stan­dard RBAC models, Constraint 2 means that if p<p ' then .R(p) ..R(p ' ).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Although our approach is lightweight and practical, it does not provide the same degree of isolation as full-virtualization would do.
Sentence 2: trustDroid only mitigates kernel­level attacks by restricting access to the .le-system, but in general, it cannot prevent an adversary from compromising the Trusted Computing Base (TCB), which for TrustDroid includes the underlying Linux kernel and the Android mid­dleware (see Section 3.3).
Relationship: entailment
Domain: Security and privacy
Sentence 1: It is important to note that the e.ect of a policy is directly de­pendent on the RDF graph it receives as input, and further­more, the e.ect may be di.erent from the original e.ect the policy was intended to achieve.
Sentence 2: the success of a policy rule (which is implemented as a SPARQL query) returning a particular set of RDF triples is dependent on the transformation step at which the rule was applied.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Therefore, in addition to recording S0 and I, for any moment j when Sj' Sj , we need to = record the state change .j = Sj - Sj-1.
Sentence 2: then the new transition function fr'is de.ned as below: '''Si'-1 +.i if .i= null Si = fr(Si-1,Ii-1, .i)= f'(Si-1,Ii-1) Otherwise during replay, whenever a state change .i has been recorded for time i, we will directly apply the recorded state change such that Si'= Si'-1 +.i.
Relationship: entailment