(Withdrawn) Collaborative security initiatives are increasingly often advocated to improve timeliness and effectiveness of threat mitigation. Among these, collaborative predictive blacklisting (CPB) aims to forecast attack sources based on alerts contributed by multiple organizations that might be targeted in similar ways. Alas, CPB proposals thus far have only focused on improving hit counts, but overlooked the impact of collaboration on false positives and false negatives. Moreover, sharing threat intelligence often prompts important privacy, confidentiality, and liability issues. In this paper, we first provide a comprehensive measurement analysis of two state-of-the-art CPB systems: one that uses a trusted central party to collect alerts [, Infocom'10] and a peer-to-peer one relying on controlled data sharing [, DIMVA'15], studying the impact of collaboration on both correct and incorrect predictions. Then, we present a novel privacy-friendly approach that significantly improves over previous work, achieving a better balance of true and false positive rates, while minimizing information disclosure. Finally, we present an extension that allows our system to scale to very large numbers of organizations.
@cite_8 introduce a few privacy-preserving protocols based on secure multiparty computation (MPC) for aggregation of network statistics. This is also explored in @cite_0 , where entities send encrypted data to a central repository that aggregates contributions. However, statistics only identify the most prolific attack sources and yield global models, which, as discussed in @cite_32 , miss a significant number of attacks and yield poor prediction performance. @cite_16 introduce an inference algorithm, BotGrep, to privately discover botnet hosts and links in network traffics, relying on Private Set Intersection @cite_15 . @cite_10 propose a game-theoretic model for software vulnerability sharing between two competing parties. Their protocol relies on a private set operation (PSO) technique to limit the amount of information disclosed. However, it does not scale for more than two entities. Finally, @cite_19 focus on collaborative predictive blacklisting based on a pairwise controlled data sharing approach. They focus on identifying which metrics (e.g., number of common attacks) can be used to privately estimate the benefits of collaboration between two organizations, rather than proposing a deployable system. In fact, as discussed later, their pairwise approach does not scale to many organizations.
@cite_8 @cite_32 @cite_0 @cite_19 @cite_15 @cite_16 @cite_10
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Secure multiparty computation (MPC) allows joint privacy-preserving computations on data of multiple parties. Although MPC has been studied substantially, building solutions that are practical in terms of computation and communication cost is still a major challenge. In this paper, we investigate the practical usefulness of MPC for multi-domain network security and monitoring. We first optimize MPC comparison operations for processing high volume data in near real-time. We then design privacy-preserving protocols for event correlation and aggregation of network traffic statistics, such as addition of volume metrics, computation of feature entropy, and distinct item count. Optimizing performance of parallel invocations, we implement our protocols along with a complete set of basic operations in a library called SEPIA. We evaluate the running time and bandwidth requirements of our protocols in realistic settings on a local cluster as well as on PlanetLab and show that they work in near real-time for up to 140 input providers and 9 computation nodes. Compared to implementations using existing general-purpose MPC frameworks, our protocols are significantly faster, requiring, for example, 3 minutes for a task that takes 2 days with general-purpose frameworks. This improvement paves the way for new applications of MPC in the area of networking. Finally, we run SEPIA's protocols on real traffic traces of 17 networks and show how they provide new possibilities for distributed troubleshooting and early anomaly detection. We consider decentralized estimation of a noise-corrupted deterministic signal in a bandwidth-constrained sensor network communicating through an insecure medium. Each sensor collects a noise-corrupted version, performs a local quantization, and transmits a 1-bit message to an ally fusion center through a wireless medium where the sensor outputs are vulnerable to unauthorized observation from enemy third-party fusion centers. In this paper, we introduce an encrypted wireless sensor network (eWSN) concept where stochastic enciphers operating on binary sensor outputs are introduced to disguise the sensor outputs, creating an eWSN scheme. Noting that the plaintext (original) and ciphertext (disguised) messages are constrained to a single bit due to bandwidth constraints, we consider a binary channel-like scheme to probabilistically encipher (i.e., flip) the sensor outputs. We first consider a symmetric key encryption case where the "0" and "1" enciphering probabilities are equal. The key is represented by the bit enciphering probability. Specifically, we derive the optimal estimator of the deterministic signal approached from a maximum-likelihood perspective and the Cramer-Rao lower bound for the estimation problem utilizing the key. Furthermore, we analyze the effect of the considered cryptosystem on enemy fusion centers that are unaware of the fact that the WSN is encrypted (i.e., we derive the bias, variance, and mean square error (MSE) of the enemy fusion center). We then extend the cryptosystem to admit unequal enciphering schemes for "0" and "1", and analyze the estimation problem from both the prospectives of ally (that has access to the enciphering keys) and (third-party) enemy fusion centers. The results show that when designed properly, a significant amount of bias and MSE can be introduced to an enemy fusion center with the cost to the ally fusion center being a marginal increase [factor of (1-Omega1-Omega0 )-2, where 1-Omegaj, j=0, 1 is the "j" enciphering probability in the estimation variance (compared to the variance of a fusion center estimate operating in a vulnerable WSN). Many deployments of secure multi-party computation (MPC) in practice have used information-theoretic three-party protocols that tolerate a single, semi-honest corrupt party, since these protocols enjoy very high efficiency. We propose a new approach for secure three-party computation (3PC) that improves security while maintaining practical efficiency that is competitive with traditional information-theoretic protocols. Our protocol is based on garbled circuits and provides security against a single, malicious corrupt party. Unlike information-theoretic 3PC protocols, ours uses a constant number of rounds. Our protocol only uses inexpensive symmetric-key cryptography: hash functions, block ciphers, pseudorandom generators (in particular, no oblivious transfers) and has performance that is comparable to that of Yao's (semi-honest) 2PC protocol. We demonstrate the practicality of our protocol with an implementation based on the JustGarble framework of (S&P 2013). The implementation incorporates various optimizations including the most recent techniques for efficient circuit garbling. We perform experiments on several benchmarking circuits, in different setups. Our experiments confirm that, despite providing a more demanding security guarantee, our protocol has performance comparable to existing information-theoretic 3PC. When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper, we address the privacy preserving set intersection (PPSI) problem, in which each of the N parties learns no elements other than the intersection of their N private datasets. We propose an efficient protocol in the malicious model, where the adversary may control arbitrary number of parties and execute the protocol for its own benefit. A related work in [12] has a correctness probability of ( v;1)ldquo (f is the size of the encryption scheme's plaintext space), a computation complexity of' 0(N2 S2lgf) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a correctness probability iquest C a -1)JV 1 plusmnmiddotd achieves a computation cost of 0 c2S2lgM) (c is the number of malicious parties and c < N eurordquo I).