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Novel trusted hardware extensions such as Intel's SGX enable user-space applications to be protected against potentially malicious operating systems. Moreover, SGX supports strong attestation guarantees, whereby remote parties can be convinced of the trustworthy nature of the executing user-space application. These developments are particularly interesting in the context of large-scale privacy-preserving...
Collaborative data mining has become very useful today with the immense increase in the amount of data collected and the increase in competition. This in turn increases the need to preserve the participants' privacy. There have been a number of approaches proposed that use Secret Sharing for privacy preservation for Secure Multiparty Computation (SMC) in different setups and applications. The different...
Business models based on offering free services to people in exchange for their data are gaining importance and prevalence. The most prominent examples are social networks and, more recently, mobile social networks. However, this trend is endangering users' privacy. We do not discuss the ethical and legal issues derived from this business model. Notwithstanding, we believe that users might have better...
Message forwarding is a fundamental brick to spread information among users in opportunistic networks. In this paper, we consider the recently proposed interest-casting networking primitive for opportunistic networks, according to which a packet generated by a sender should be delivered to all users in the network — potentially unknown to the sender — sharing similar interests. However, the current...
Now-a-days privacy has become a major concern; the goals of security like confidentiality, integrity and availability do not ensure privacy. Data mining is a threat to privacy. Researchers today focus on how to ensure privacy while performing data mining task. As Data mining algorithms are typically complex and furthermore the input usually consists of massive data sets, the generic protocols in such...
There has been concern over the apparent conflict between privacy and data mining. Attribute reduction is one of the most important contributions of rough set theory to data mining. In this paper, we address the issue of privacy preserving attribute reduction. Specifically, we consider a scenario in which two parties owning private data, wish to run a attribute reduction algorithm on the union of...
Secure scalar product protocol is an important fundamental protocol in secure multi-party computation. Serving as a basic building block for many other secure protocols, it is widely used in data mining, statistical analysis and scientific computation. Based on additive homomorphism public key cryptosystem, we develop a new secure scalar product protocol under semi-honest model with low communication...
In this paper, we consider a scenario in which two parties are interested to find, in secure multiparty computation, the shortest path in a public graph. In particular, we consider the case in which, Alice knows the weights on the edges of the graph, Bob knows an heuristic to find the best path and together they want to discover the walk between two given nodes in privacy preserving way. We present...
In today’s distributed computing environment multiple parties compute some function of their private inputs. In such a scenario privacy preservation of such inputs is a matter of great concern because each party is also worried about the privacy of their inputs. This subject is evolved as Secure Multiparty Computation (SMC). The protocols proposed in this paper allow multiple parties to get maximum...
Abstract- Data mining technique, classification and prediction has improved and used in medical domain to helping medical practitioner in making their decisions. As medical data is highly sensitive to personal information of human being, so it is desired to keep private. There are many approaches for classification which have been adapted for privacy preserving in medical data which are based on data...
Collaborative business applications can use secure multiparty computation to preserve input privacy. These applications need protocols that provide all the basic operations with integers and rational numbers and allow secure composition and efficient application development. Secure computation with rational numbers is a long-standing open problem. We present in this paper several components of a protocol...
Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is often desirable. Secure multiparty computation (SMC) provides methods that in principle allow such computation without leaking any information. However,...
The nearby-friend problem for a user in the context of location based services is to learn the location of a nearby-friend only if (s)he is actually nearby, while simultaneously maintaining the privacy of their respective locations. The existing works address the problem for two users only. In this paper, we present for the first time a solution which caters to multiple users. We use techniques from...
Computing convex hull for a given set of points is one of the most explored problems in the area of computational geometry (CG). If the set of points is distributed among a set of parties who jointly wish to compute the convex hull, each party can send his points to every other party, and can then locally compute the hull using any of the existing algorithms in CG. However such an approach does not...
Secure multiparty protocols have found applications in numerous domains, where multiple nontrusting parties wish to evaluate a function of their private inputs. In this paper, we consider the case of multiple robots wishing to localize themselves, with maps as their private inputs. Though localization of robots has been a well studied problem, only recent studies have shown how to actively localize...
Reputation systems are designed for reducing the risk entailed in interactions among total strangers in electronic marketplaces. Such systems can be used to collect and aggregate feedback on the past behavior of participants in electronic transactions, so as to derive reputation scores assumed to predict likely future behavior.The security of reputation system is vital for the system to run correctly,...
Secure multi-party computation has been a hot research topic of cryptograhy for about two decades, and the convex hulls problem is a special case of it. However, the precise convex hulls will certainly expose all vertexes and even bring about unfairness. Therefore the practical approximate convex hulls are in need. In this paper, we summarize and discuss the convex hulls problem, and then we present...
The reason for using distributed constraint satisfaction algorithms is often to allow agents to find a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this information. This paper describes some simple techniques that can be used with DisCSP algorithms such as DPOP, and provide sensible privacy guarantees...
Privacy is ultimately important, and there is a fair amount of research about it. However, few empirical studies about the cost of privacy are conducted. In the area of secure multiparty computation, the scalar product has long been reckoned as one of the most promising building blocks in place of the classic logic gates. The reason is not only the scalar product complete, which is as good as logic...
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