25613636
Efficient distributed SAT and SAT-based distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Even with the recent development of improved SAT solvers, the memory limitation of a single server rather than time can become a bottleneck for doing deeper BMC search for large designs. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present (a) a method for distributed SAT over a network of workstations using a Master/Client model where each Client workstation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, (b) a method for distributing SAT-based BMC using the distributed SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogeneous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with ∼13 K FFs and ∼0.5 million gates, the non-distributed BMC on a single workstation (with 4 GB memory) ran out of memory after reaching a depth of 120; on the other hand, our SAT-based distributed BMC over 5 similar workstations was able to go up to 323 steps with a communication overhead of only 30%.
8920735
Partition-based decision heuristics for image computation using SAT and BDDs
Methods based on Boolean satisfiability (SAT) typically use a conjunctive normal form (CNF) representation of the Boolean formula, and exploit the structure of the given problem through use of various decision heuristics and implication methods. We propose a new decision heuristic based on separator-set induced partitioning of the underlying CNF graph. It targets those variables whose choice generates clause partitions with disjoint variable supports. This can potentially improve performance of SAT applications by decomposing the problem dynamically within the search. In the context of a recently proposed image computation method combining SAT and BDDs, this results in simpler BDD subproblems. We provide algorithms for CNF partitioning - one based on a clause-variable dependency matrix, and another based on standard hypergraph partitioning techniques, and also for the use of partitioning information in decision heuristics for SAT. The effectiveness of the proposed partition-based heuristic is shown with practical results for reachability analysis of benchmark sequential circuits.
6254649
Disjunctive Image Computation for Embedded Software Verification
Finite state models generated from software programs have unique characteristics that are not exploited by existing model checking algorithms. In this paper, we propose a novel disjunctive image computation algorithm and other simplifications based on these characteristics. Our algorithm divides an image computation into a disjunctive set of easier ones that can be performed in isolation. Hypergraph partitioning is used to minimize the number of live variables in each disjunctive component. We use the live variables to simplify transition relations and reachable state subsets. Our experiments on a set of real-world C programs show that the new algorithm achieves orders-of-magnitude performance improvement over the best known conjunctive image computation algorithm
361158
Efficient Distributed SAT and SAT-Based Distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Interestingly, with the recent development of improved SAT solvers, it is frequently the memory limitation of a single server rather than time that becomes a bottleneck for doing deeper BMC search. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present: a) a method for distributed-SAT over a network of workstations using a Master/Client model where each Client worsktation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, b) a method for distributing SAT-based BMC using the distributed-SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogenous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with 13K FFs and 0.5M gates, the non-disributed BMC on a single workstation (with 4 Gb memory) ran out of memroy after reaching a depth of 120; on the otherhand, our SAT-based distributed BMC over 5 similar workstations was able to go upto 323 steps with a communication overhead of only 30%.
193663
SAT-Based Image Computation with Application in Reachability Analysis
Image computation nds wide application in VLSI CAD, such as state reachability analysis in formal veri cation and synthesis, combinational veri cation, combinational and sequential test. Existing BDD-based symbolic algorithms for image computation are limited by memory resources in practice, while SAT-based algorithms that can obtain the image by enumerating satisfying assignments to a CNF representation of the Boolean relation are potentially limited by time resources. We propose new algorithms that combine BDDs and SAT in order to exploit their complementary bene ts, and to o er a mechanism for trading o space vs. time. In particular, (1) our integrated algorithm uses BDDs to represent the input and image sets, and a CNF formula to represent the Boolean relation, (2) a fundamental enhancement called BDD Bounding is used whereby the SAT solver uses the BDDs for the input set and the dynamically changing image set to prune the search space of all solutions, (3) BDDs are used to compute all solutions below intermediate points in the SAT decision tree, (4) a ne-grained variable quanti cation schedule is used for each BDD subproblem, based on the CNF representation of the Boolean relation. These enhancements coupled with more engineering heuristics lead to an overall algorithm that can potentially handle larger problems. This is supported by our preliminary results on exact reachability analysis of ISCAS benchmark circuits.
13092959
Whodunit? Causal Analysis for Counterexamples
Although the counterexample returned by a model checker can help in reproducing the symptom related to a defect, a significant amount of effort is often required for the programmer to interpret it in order to locate the cause. In this paper, we provide an automated procedure to zoom in to potential software defects by analyzing a single concrete counterexample. Our analysis relies on extracting from the counterexample a syntactic-level proof of infeasibility, i.e., a minimal set of word-level predicates that contradict with each other. The procedure uses an efficient weakest pre-condition algorithm carried out on a single concrete execution path, which is significantly more scalable than other model checking based approaches. Unlike most of the existing methods, we do not need additional execution traces other than the buggy one. We use public-domain examples to demonstrate the effectiveness of our new algorithm.
17249203
Privacy-Preserving Queries on Encrypted Data
Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.
2702750
A new look at multimedia synchronization in distributed environments
A multimedia system requires to handle automatically and uniformly the generation, manipulation, presentation, storage, and communication of independent discrete and continuous media data. The synchronization between various kinds of media data is a key issue in an integrated multimedia system, and has drawn a considerable attention. In this paper we investigate the synchronization problem in distributed multimedia environments and propose a new approach. Based on a globally synchronized clock, we present an isochronous clock-driven protocol for multimedia synchronization in a distributed environment. We show that the isochronous approach is simple, easy to understand, and more importantly, can guarantee the temporal ordering (including causal order and /spl Delta/-causal order). We believe that with the new developments in the improved NTP over Internet and very high accuracy of synchronized clocks over ATM and LANs, the clock-based distributed synchronization would provide a promising, yet simple and intuitive, alternative to the existing approaches.
17075655
Privacy-enhancing k-anonymization of customer data
In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the entire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results.
11052169
Privacy-Preserving Classification of Customer Data without Loss of Accuracy
Privacy has become an increasingly important issue in data mining. In this paper, we consider a scenario in which a data miner surveys a large number of customers to learn classiflcation rules on their data, while the sensitive attributes of these customers need to be protected. Solutions have been proposed to address this problem using randomization techniques. Such solutions exhibit a tradeofi of accuracy and privacy: the more each customer’s private information is protected, the less accurate result the miner obtains; conversely, the more accurate the result, the less privacy for the customers. In this paper, we propose a simple cryptographic approach that is e‐cient even in a many-customer setting, provides strong privacy for each customer, and does not lose any accuracy as the cost of privacy. Our key technical contribution is a privacy-preserving method that allows a data miner to compute frequencies of values or tuples of values in the customers’ data, without revealing the privacy-sensitive part of the data. Unlike general-purpose cryptographic protocols, this method requires no interaction between customers, and each customer only needs to send a single ∞ow of communication to the data miner. However, we are still able to ensure that nothing about the sensitive data beyond the desired frequencies is revealed to the data miner. To illustrate the power of our approach, we use our frequency mining computation to obtain a privacypreserving naive Bayes classifler learning algorithm. Initial experimental results demonstrate the practical e‐ciency of our solution. We also suggest some other applications of privacy-preserving frequency mining.
40550352
Partial Relevance in Interactive Facial Image Retrieval
For databases of facial images, where each subject has only a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A novel method is proposed to relieve this problem by applying partial relevance to the interactive retrieval. This work extends an existing content-based image retrieval system, PicSOM, by relaxing the relevance criterion in the early rounds of the retrieval. Moreover, we apply linear discriminant analysis as a preprocessing step before training the Self-Organizing Maps (SOMs) so that the resulting SOMs have stronger discriminative power. The results of simulated retrieval experiments suggest that for semantic classes such as “black persons” or “bearded persons” the first image which depicts the target subject can be obtained three to six times faster than by retrieval without the partial relevance.