sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: Timeliness can be more formally de.ned as the amount of information an attacker learns about the state of the dy­namic part of the system based on all of the information available about its past states.
Sentence 2: if the amount of time it takes to successfully attack the system is T , the amount of information the attacker has about the state of the system at time of attack given all the past history of the system should be zero.
Relationship: entailment
Domain: Security and privacy
Sentence 1: It is interesting to note that, in the presence of enough malicious nodes, despite the sys­tem converging in the sense that the relative errors at each node stabilize, these errors are so high that a great variation of the coor­dinates of a node barely affects the associated error.
Sentence 2: the coordinates of the nodes keep showing great variations and do not stabilize but the error introduced by such constant movement is stable because there is already so much chaos in the system.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this paper we extend those ideas about software up­datebyoutlining anarchitecturefora simpleproperty-based sealing andbypresenting aproof of concept implementation based on OpenTC design principles, namely the combined use of Trusted Computing and virtualization.
Sentence 2: the use of properties was .rst proposed to mitigate the problems related with remoteattestation(referto[9,10] for 2 memory isolation and communications subject to .ow control policies.
Relationship: entailment
Domain: Security and privacy
Sentence 1: This means that for malicious peers to double-spend a BTC without being detected, they would not only have to redo all the work required to compute the block where that BTC was spent, but also recompute all the subsequent blocks in the chain.
Sentence 2: this 3the block contains correctly formed transactions that have not been previously spent, and has a correct PoW.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Summary of Results: By running ClickMiner on the network trace recorded during the user-generated browsing session described above, we were able to reconstruct the full chain of user-browser in­teractions that led the user to the malware download.
Sentence 2: clickMiner reduced the amount of information to be analyzed by 3MD5: c94f917fdc39dfb7245ebdd674b2bdf8  a forensic analyst from 316 HTTP exchanges to only 6 click graph nodes, as shown in Figure 6.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Our research on graphical passwords has shown that as­signed graphical passwords are indeed more memorable than assigned text passwords [25], and that the image cues asso­ciated with some graphical passwords can help users distin­guish multiple passwords from each other [5].
Sentence 2: we developed Image PassTiles, a cued-recall graphical pass­word system where the user is shown an image overlain by a grid.
Relationship: entailment
Domain: Security and privacy
Sentence 1: It also has an additional rule for expressions, for typing signature veri.cations; this rule is identical to rule OP except for its cryptographic data types: OPVER f y : t(e) f m : Sig t(es)(e) f kv : Kv F K(e) F(t)= t(es) fV(t + y, m, kv): Data (e) Rule PROBFUN is the generic rule for typing probabilistic func­tions; it requires that all variables have Data types and prevents explicit .ows from the parameters Ay to the results Ax.
Sentence 2: pROBFUN applies to the functions S, E, and D in case we do not rely on cryptographic assumptions.
Relationship: entailment
Domain: Security and privacy
Sentence 1: HOMOMORPHIC MACS WITH EFFICIENT VERIFICATION In this section, we describe our construction of homomorphic MACs with e.cient veri.cation for multi-labeled programs as introduced in Section 3.3.
Sentence 2: the following theorem summarizes the main result of this work which is obtained by combining the EVH-MAC construction (see be­low) and our concrete instantiation of the PRF with amor­tized closed-form e.ciency based on the Decision Linear as­sumption (Section 4.3).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Consequently, a list is maintained for each node, rat­ing from 0 to 5 the number of times the corresponding node has acted illegally.
Sentence 2: the value of 0 indicates a node that operates normally and has never been detected for malicious activities, whereas, the value of 5 denotes that the node has already been detected 5 times for attacking actions.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Since the PI N O C C H I O evaluation key size is proportional to the size of the circuit and the certifying algorithm has a more ef.cient circuit im­plementation than the one of BFS in Figure 3, the server storage for the certifying algorithm is much smaller.
Sentence 2: the server storage is reduced by 32340× on a graph with 1,000 nodes and the gap grows on larger graphs.
Relationship: entailment
Domain: Security and privacy
Sentence 1: UAQ: A Framework for User Authorization Query Processing in RBAC extended with Hybrid Hierarchy and Constraints Yue Zhang University of Pittsburgh 135 N. North Bellefield Ave. Pittsburgh PA 15260 1-412-624-4099  yuz20@pitt.edu ABSTRACT A key issue in RBAC systems is how to efficiently handle the user authorization process.
Sentence 2: whether or not to grant a user s request to acquire a set of requested permissions or to activate a set of requested roles in a single session.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Proof: It is well known and easy to see that conjunctive queries with inequalities are preserved under one-to-one homomorphisms.
Sentence 2: if his a one-to-one homomorphism from Jto Jm2, and if is a R-ary conjunctive query with inequalities and y(a(yod dodoa)n n(Jy), then (hn((ayo)od d doh))n(m2Jy)o.
Relationship: entailment
Domain: Security and privacy
Sentence 1: )= s, where y .Y  Pr(s)= Pr(s|z), where z .Z We will focus on solving the secure coding problem at the level of bits.
Sentence 2: our goal is to .nd f and f, such that for any m-bit source message s, j  encodes s via f into an n-bit message x, k decodes s via f and the knowledge of ., while e guesses s correctly with a probability that not better than 1 2m even if it knows f and f (regarded as perfect secrecy).
Relationship: entailment
Domain: Security and privacy
Sentence 1: where F[Verify, M, PK]Ati~ represents Alice's output, F[Verify, M, PK]Bob represents Bob's output, and .£ is a special symbol.
Sentence 2: our goal is that Alice learns nothing and Bob learns F[Verif~c,M, PK]sob(P,a) without learning anything else.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For example, in DLMS/COSEM, the DKs are defined as ones that are used for the encryption of unicast xDLMS APDU.
Sentence 2: the DKs are unique session secret keys between two communication devices, which protect xDLMS APDU transmitted between a COSEM client, Central DCS or Concentrator, and a COSEM server.
Relationship: entailment
Domain: Security and privacy
Sentence 1: As the proof of Theorem 1 does not use the marking condition, innocent users are not likely to be accused even if the pirates can .nd the positions of the .ngerprint code and thus they can break the marking condition.
Sentence 2: we can be reasonably sure that any accused user is a member of the group of pirates even if we do not know any bound on the size or power of this group.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Furthermore, the expected subrange interval lies symmetrically around N+1 .For |D| = 2, this reduces 2 the IND-OCPA properties of the random subrange selection approach to that of the approach used in the subrange.
Sentence 2: it is again possible to adopt the argument given in [12], to show that for this speci.c domain size random o.set addition provides a higher level of IND-OCPA than random subrange selection in combination with the ideal object or a random uniform sampling approach.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Table 1: Error rates per click and completion time per game type Game Type Completion Time (s) mean (std dev) Error Rate Per Click mean Ships 4.51 (1.00) 0.04 Animals 9.10 (0.96) 0.05 Parking 4.37 (0.90) 0.09 Shapes 5.26 (0.59) 0.03 Error Rates: An important result is that all the tested games yielded 100% accuracy (overall error rate of 0%).
Sentence 2: none of the participant failed to complete any of the games within the time out.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Espe­cially, we inject the Consent Management Service, Policy Composition Service, and EHR Authorization &#38; Selection Service as the major system modules to convey the core fea­tures of our proposed approach.
Sentence 2: the Consent Management Service enables the patient control by collect­ing and verifying the patient s access control policies as en­capsulated in consents.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In particular, it can provide the following ben­e.ts: 1) it provides a designer with more realistic measure of SCA resistance at design time; 2) it enables integration of the right level of protection; and 3) as we observe later, the PDN causes variation in SCA resistance with operating frequency, which helps a designer to choose right frequency of operation and/or accomplish balanced protection across frequency spectrum.
Sentence 2: the paper makes the following key contribu­tions.
Relationship: entailment
Domain: Security and privacy
Sentence 1: ., k - 1, where each yi is chosen randomly from Zq and A = rk-1 A2i i , and showing i=0 that each xi hidden in com(xi) is either 0 or 1.
Sentence 2: the exact statement that the user proves is of the form: PK{(a, ß, ., d, d0, .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Key-evolving signature scheme is a quadruple of algo­rithms KESig =(Gen,Upd,Sign,Vf), where: KESig.Gen, the probabilistic key generation algorithm.
Sentence 2: input: a security parameter k. N (given in unary as 1k); and the total number of periods T; Output:a pair (SK 0,PK ), the initial secret key and the public key;  4randomness of the evolution was optional for forward-security, but is crucial for tamper-evidence; while one-wayness of the evolution was of central value for forward-security, but is optional for tamper-evidence.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Hence, to prove security we show that solving this variant of DUE assumption (denoted by DUE1) implies a solution to the original DUE problem.
Sentence 2: given a random (m, n, d) graph with a planted shrinking set, an algorithm for DUE1 can be used to dis­tinguish with some constant advantage between nodes that participate in the shrinking set to other nodes.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Before turning our attention to this computation of z, we observe that we can choose, without loss of security, the modulus of the additive-splitting to be the same as the one for the homomorphic encryption system.
Sentence 2: if arith­metic is modulo T for the homomorphic encryption, then (1) we can assume that P1 (resp., P2 ) initially has xand (1) (2) (2) (1) (2) y(xand y), such that x+ xmod T = x, and (1) (2) y+ ymod T = y.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We developed a Fine-Grained Detector de­rived from BotMiner [7] and BotSni.er [5], and we plugged it into our botnet detection framework.
Sentence 2: we used two components: i) an implementation of the malicious Figure 2: Packet Sampling Architecture activities detector derived from BotMiner s A-Plane moni­tor, which can identify groups of similar malicious activities based on the attack features (e.g., the scanned port, the exploits or binary content), and ii) BotSni.er s IRC-based botnet detection module.
Relationship: entailment
Domain: Security and privacy
Sentence 1: If A later corrupts some players in pid, then due to the way corruptions are handled by S this will not intro­duce any noticeable di.erence from the point of view of Z (again relying on Claim 6, the assumed AKE­security of 1, and the pseudorandomness of F ).
Sentence 2: if a player U ' .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Now, nodes act like switches, which can block or unblock enforced .ows.
Sentence 2: in periodic role enabling/disabling, each role is associated with a periodic expression that indicates the ac­tivation period of the role, e.g., all ·Y ear +{1, 4}·Months b 2 represents the set of intervals starting at the .rst and fourth month of every year, and having a duration of two months [6].
Relationship: entailment
Domain: Security and privacy
Sentence 1: EXPERIMENTAL RESULTS In this section, we report on our preliminary experiments towards building a generic toolkit for evaluating both sani­tization methods and inference attacks on geolocated data.
Sentence 2: we describe some clustering algorithms and heuristics, which can be used as inference attack, and evalu­ate their e.ciency for the identi.cation of POIs, even after the application of sanitization mechanisms such as sampling and perturbation.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Further, this communica­tion channel is restricted to be a stream of bits whose semantics is well-understood.
Sentence 2: each 0 bit in the stream denotes a vio­lation of regulatory compliance, while a 1 bit denotes otherwise.1 The client can setupa user daemon(e.g., within Udom0) that is awakenedby the SSChypervisor uponevery new bit transmitted bytheMTSDoverthis channel.An honest clientthatdoesnotvio­late the provider s regulatory compliance policies should therefore only expect to see a stream of 1s transmitted to Sdom0.
Relationship: entailment
Domain: Security and privacy
Sentence 1: FrSIG cov­ers six kinds of security levels, i.e., combination of two lev­els of unforgeability and three levels of anonymity.
Sentence 2: (uf , anon) represents a security level as uf is param­eterized by eUF and sUF, and anon is parameterized by basic anonymity, anonymity against attribution attacks and anonymity against full key exposure.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2.3.4 Availability Data transmission over ad-hoc wireless and sensor net­works as well as unprotected radio-frequency identi.cation (RFID) tags are known to be vulnerable to active attacks such as tra.c analysis, spoo.ng and denial of service [28, 27].
Sentence 2: limited bandwidth of the ad-hoc commu­nication networks that are used in ubicomp applications ex­poses such systems as targets for denial-of-service attacks.
Relationship: entailment
Domain: Security and privacy
Sentence 1: As of April 20, 2010, 54,429 of the retested devices are still publicly acces­sible, out of which 52,661 devices remain vulnerable.
Sentence 2: approximately 96.75% of accessible vul­nerable devices are still vulnerable after a 4 month period, and factory default credentials have been removed from only 3.25% of the same set of devices.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The strength of our mechanism relies on the enhancement of the known audio/image/video watermarking algorithms [7, 10, 11, 17] with the usage of a semantically secure watermark as in the case of compressed audio watermarking algorithm [13, 14].
Sentence 2: instead of using a simple bit sequence as a watermark, we apply a semantic watermark which is a bit sequence that represents a certain combinatorial object with a specific characteristic known only to its creator.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For this purpose, we model security sce­narios by using defense trees (an extension of attack trees) and preferences over countermeasure using Conditional Pref­erence networks (CP-nets for short).
Sentence 2: we in­troduce two di.erent metho ds for the composition of pref­erences: the and-comp osition and the or-comp osition.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this context, an emerging trend is the integration of smartphones into modern automotive systems and applications such as ac­cess control to unlock, con.gure and start vehicles [41, 39, 47].
Sentence 2: the NFC interface is well-suited for such applications due to its short nominal communication range (of a few centimeters) providing basic assurance of the user s physical proximity.
Relationship: entailment
Domain: Security and privacy
Sentence 1: By not only directly observing the bot clients but also the reconstructed botnet C&#38;C server, we were able to wear the shoes of the bot­master and were thus able to identify some of the performance and design challenges that botnet creators and botmasters must face.
Sentence 2: what would have been a textbook solution to ensure data con.dentiality and integrity, i.e.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Namely, when data remotely measured by patients is used by tele-health services or in the medical professional world, healthcare providers need to place greater trust in the information that patients report.
Sentence 2: in or­der to make sound medical decisions based on this remotely measured biomedical signals, they have to be ensured that a measurement contains as little noise as possible, the amount of noise being an indication of the quality of the signal.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Unfortunately, while this process prevents explicit misuse of participants DNA data, it turns out to be insuf.cient for deterring information leaks in a more implicit way.
Sentence 2: this paper reports a surprising .nding of our research: even the test statistics computed over a small set of SNPs, like those routinely published in GWAS papers, could reveal a substantial amount of genetic information about participants, and even lead to disclosure of their identities.
Relationship: entailment
Domain: Security and privacy
Sentence 1: If num(i,Xj) is not larger than the number of the corresponding pixel value i in S -i.e., num(i,Xj) = .i .i=0,1,··· ,255, then all the ciphertexts of block Xj are di.erent from each other, no matter their plaintexts are the same or not.
Sentence 2: the encryption procedure of our  (a) (b)  (c) Figure 2: The function of RandPermut([Sß ]).
Relationship: entailment
Domain: Security and privacy
Sentence 1: [12] have suggested that it may result in an overoptimistic estimate of the malware clustering ac­curacy.
Sentence 2: limiting the reference clustering to samples for which a majority voting-based consensus on the family label can be reached, and discarding the remaining ones, may reduce the reference clusters to only include easy to cluster malware sam­ples (i.e., clear-cut cases of malware samples that are very similar to each other) [12], thus potentially causing the accuracy of the malware clustering results to be largely overestimated.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Because of the inconsistency of the Kaspersky labels (some Kaspersky label groupings, such as the jorik family, represent a very diffuse range of malware, whereas others, such as webprefix represent a very specific set of variants) we heuristically selected four Kaspersky defined malware classes that we believe share a similar range of behavioral traits.
Sentence 2: we selected the xtoober , jorik and vbkrypt families, alongside a random selection of malware samples Kaspersky labeled as unknown samples.
Relationship: entailment
Domain: Security and privacy
Sentence 1: They pointed out that many prior proxy re-encryption schemes (before their paper) cannot satisfy the intuition of anonymity: an adversary cannot learn identities by observing su.cient ciphertexts (before or after re-encryption) and re-encryption keys, even it colludes with some corrupted users.
Sentence 2: for any probabilistically polynomial time adversary, it cannot distinguish a valid re-encryption key for a pair of users, from a random one selected from the re-encryption key space, although it can access re-encryption key generation oracle and re-encryption oracle (perhaps decryption oracle addi­tionally).
Relationship: entailment
Domain: Security and privacy
Sentence 1: For example, PIO overlapping is similar to the PIO con.guration space attack, but in this case the attacker can only recon.gure the con.guration space of a speci.c device she controls.
Sentence 2: by changing the PIO BAR register of a directly assigned PCI/PCIe device, an attacker can overwrite the BAR value with the one of another device at­tached to another VM.
Relationship: entailment
Domain: Security and privacy
Sentence 1: WATCHER -This is a secondary thread that man­ages: a) The Qemu-KVM startup; b) The registration of the Qemu-KVM pid into KvmSec; c) The abnormal termination of Qemu-KVM;  Communication channel with Qemu: Since both the DM and the Qemu processes are executed in userspace, we can use any of the Linux System V IPC facilities for Inter-Process Communication.
Sentence 2: we chose to make use of a named PIPE (or FIFO).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Returning to the body of the loop, the next step is to make sure that for every Pi (line 3) its threshold check (Figure 4) will not reject Pj .
Sentence 2: given the query q and the estimated knowledge of Pj, we make sure that the answer to the query will not reveal too much about Pi s secret xi (where by too much we mean Pj s certainty about Pi s possible secret exceeds threshold ti).
Relationship: entailment
Domain: Security and privacy
Sentence 1: A policy language which introduces a decision to indicate the occurrence of errors in the policy evaluation process is XACML v2 [1].
Sentence 2: xACML v2 uses decision set {Permit, Deny, NA, Indeterminate}, where Permit repre­sents the permit decision, Deny the deny decision, NA rep­resents that the policy is not applicable to the given query, and Indeterminate indicates that the policy decision point is unable to evaluate the query because of some error (e.g., missing attributes, division by zero, network errors while retrieving policies, syntax errors in the query or in the pol­icy).
Relationship: entailment
Domain: Security and privacy
Sentence 1: FRAMEWORK OVERVIEW AND THREAT MODEL 2.1 System Framework Overview The basic requirement of OS kernel integrity protection prob­lem is to control accesses to sensitive kernel objects (e.g., the ker­nel text, the system call table, the interrupt descriptor table) in a real-time manner.
Sentence 2: authorization decisions should be based on the real-time attribute values of requesting subjects and target objects.
Relationship: entailment
Domain: Security and privacy
Sentence 1: More rigorous government information assurance examples include the Common Criteria (CC) [1] standard ISO/IEC 15408, a framework in which computer system users can specify their security requirements, vendors can then implement and / or make claims about the security attributes of their products, and testing laboratories can evaluate the products to determine if they actually meet the claims.
Sentence 2: cC [1] provides assurance that the process of specification, implementation and evaluation of a computer security product has been conducted in a rigorous and standard compliant manner.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Let V iewB((p, s), t) be a random vari­able representing Bob s view during the real execution of SPH-PSM.
Sentence 2: we say that SPH-PSM guarantees Alice s pri­vacy if there exists a Probabilistic Polynomial Time (PPT) algorithm B* such that, given Bob s output b, the following holds: {B * (t, b)}((p,s),t) =c {V iewB((p, s), t)}((p,s),t) for all possible inputs, with the knowledge of the output bit b and Bob s input, B* can e.ciently simulate the view of Bob.
Relationship: entailment