Strong (n, t, n) verifiable secret sharing scheme

A (t, n) secret sharing divides a secret into n shares in such a way that any t or more than t shares can reconstruct the secret; but fewer than t shares cannot reconstruct the secret. In this paper, we extend the idea of a (t, n) secret sharing scheme and give a formal definition on the (n, t, n) secret sharing scheme based on Pedersen’s (t, n) secret sharing scheme. We will show that the (t, n) verifiable secret sharing (VSS) scheme proposed by Benaloh can only ensure that all shares are t-consistent (i.e. any subset of t shares defines the same secret); but shares may not satisfy the security requirements of a (t, n) secret sharing scheme. Then, we introduce new notions of strong t-consistency and strong VSS. A strong VSS can ensure that (a) all shares are t-consistent, and (b) all shares satisfy the security requirements of a secret sharing scheme. We propose a strong (n, t, n) VSS based on Benaloh’s VSS. We also prove that our proposed (n, t, n) VSS satisfies the definition of a strong VSS.     

LR-型模糊需求下供应链的质量控制与成本分担

研究LR-型模糊需求下两级供应链中可变比例成本分担和质量控制的契约设计问题. 在召回成本无分担的分散模式下, 供应链成员努力投资不足, 集中式供应链努力选择互补, 互补程度关于单位召回成本和总缺陷率递增. 引入可变比例成本分担契约, 该契约能发挥制造商与供应商的努力选择互补效应, 且双方关于努力选择的博弈存 在Nash 均衡. 当缺陷分析成本为零时, 在契约Φ 下供应链成员激励相容, 供应链的整体利润等于集中式最优利润. 确定可变分担比例解析解、缺陷率阈值. 验证结果表明, 契约Φ 的成本无效指数和质量无效指数均低于固定比例成本分担合同, 契约Φ 比固定比例成本分担合同更合理、有效.

     

Secret sharing approach for securing cloud-based pre-classification volume ray-casting

With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir’s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations – the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers.     

On the Convergence of Multicast Games in Directed Networks

We investigate the convergence of the price of anarchy after a limited number of moves in the classical multicast communication game when the underlying communication network is directed. Namely, a subset of nodes of the network are interested in receiving the transmission from a given source node and can share the cost of the used links according to fixed cost sharing methods. At each step, a single receiver is allowed to modify its communication strategy, that is to select a communication path from the source, and assuming a selfish or rational behavior, it will make a best response move, that is it will select a solution yielding the minimum possible payment or shared cost. We determine lower and upper bounds on the price of anarchy, that is the highest possible ratio among the overall cost of the links used by the receivers and the minimum possible cost realizing the required communications, after a limited number of moves under the fundamental Shapley cost sharing method. In particular, assuming that the initial set of connecting paths can be arbitrary, we show an $O(r\sqrt{r})We investigate the convergence of the price of anarchy after a limited number of moves in the classical multicast communication game when the underlying communication network is directed. Namely, a subset of nodes of the network are interested in receiving the transmission from a given source node and can share the cost of the used links according to fixed cost sharing methods. At each step, a single receiver is allowed to modify its communication strategy, that is to select a communication path from the source, and assuming a selfish or rational behavior, it will make a best response move, that is it will select a solution yielding the minimum possible payment or shared cost. We determine lower and upper bounds on the price of anarchy, that is the highest possible ratio among the overall cost of the links used by the receivers and the minimum possible cost realizing the required communications, after a limited number of moves under the fundamental Shapley cost sharing method. In particular, assuming that the initial set of connecting paths can be arbitrary, we show an O(r?r)O(r\sqrt{r}) upper bound on the price of anarchy after 2 rounds, during each of which all the receivers move exactly once, and a matching lower bound, that we also extend to W(r^k?{r})\Omega(r\sqrt[k]{r}) for any number k≥2 rounds, where r is the number of receivers. Similarly, exactly matching upper and lower bounds equal to r are determined for any number of rounds when starting from the empty state in which no path has been selected. Analogous results are obtained also with respect to other three natural cost sharing methods considered in the literature, that is the egalitarian, path-proportional and egalitarian-path proportional ones. Most results are also extended to the undirected case in which the communication links are bidirectional.     

A verifiable multi-secret sharing scheme based on cellular automata

In this paper, we propose a verifiable (t, n)-threshold multi-secret sharing scheme, based on one-dimensional cellular automata where the number of secrets is not restricted by n or t. We show that our scheme can be used to solve an open problem proposed recently in Alvarez et al. (2008) [G. Alvarez, L. Hernández Encinas, A. Martín del Rey, A multisecret sharing scheme for color images based on cellular automata, Information Sciences 178 (2008) 4382-4395].     

A practical verifiable multi-secret sharing scheme

C.-C. Yang, T.-Y. Chang, M.-S. Hwang [C.-C. Yang, T.-Y. Chang, M.-S. Hwang, A (t,n) multi-secret sharing scheme, Applied Mathematics and Computation 151 (2004) 483–490] proposed an efficient multi-secret sharing scheme based on a two-variable one-way function in 2004. But the scheme doesn't have the property of verification. A practical verifiable multi-secret sharing scheme, which is based on the YCH scheme and the intractability of the discrete logarithm, is proposed in this paper. Our scheme solves the problems in the YCH scheme; each participant chooses her/his own shadow by her/himself, so the system doesn't need a security channel and the cost of the system can be lowered. The scheme can be used in practice widely.     

An ideal multi-secret sharing scheme based on MSP

A multi-secret sharing scheme is a protocol to share m arbitrarily related secrets s₁, … , s_m among a set of n participants. In this paper, we propose an ideal linear multi-secret sharing scheme, based on monotone span programs, where each subset of the set of participants may have the associated secret. Our scheme can be used to meet the security requirement in practical applications, such as secure group communication and privacy preserving data mining etc. We also prove that our proposed scheme satisfies the definition of a perfect multi-secret sharing scheme.     

A secure Boolean-based multi-secret image sharing scheme

An (n, n) multi-secret image sharing scheme shares n secret images among n shared images. In this type of schemes, n shared images can be used to recover all n secret images, but the loss of any shared image prevents the recovery of any secret image. Among existing image sharing techniques, Boolean-based secret schemes have good performance because they only require XOR calculation. This study presents a secure Boolean-based secret image sharing scheme that uses a random image generating function to generate a random image from secret images or shared images. The proposed function efficiently increases the sharing capacity on free of sharing the random image. The use of a bit shift subfunction in the random image generating function produces a random image to meet the random requirement. Experimental results show that the proposed scheme requires minimal CPU computation time to share or recover secret images. The time required to share n secret images is nearly the time as that required to recover n secret images. The bit shift subfunction takes more computation load than the XOR subfunction needs.     

Cutting Chai, Jugaad, and Here Pheri: towards UbiComp for a global community

This paper attempts to re-imagine ubiquitous computing and technologies for populations in resource-poor, digitally unstable, and diversely literate environments. Extending UbiComp’s frame of reference to include any ICT with a ubiquitous presence, we articulate how technologies are adopted, accessed, used, and diffused in three urban slums of India. We showcase important local practices surrounding technology diffusion and their widespread implications for entrenching ICT use through sharing, learning, training, renewing, and extending use and access. We do this by discussing three main processes at the intersection of technology consumption, resource constraints, and cultural production specific to low-income communities in India: Cutting Chai or sharing technology ownership and maintenance to cut costs, Jugaad or workarounds in the face of resource constraints, and Here Pheri or gray market activity that subvert legal business processes. We also suggest a few design principles to provoke new kinds of inquiry and practice in the design and implementation of UbiComp for a global community.     

On the optimization of schedules for MapReduce workloads in the presence of shared scans

We consider MapReduce clusters designed to support multiple concurrent jobs, concentrating on environments in which the number of distinct datasets is modest relative to the number of jobs. In such scenarios, many individual datasets are likely to be scanned concurrently by multiple Map phase jobs. As has been noticed previously, this scenario provides an opportunity for Map phase jobs to cooperate, sharing the scans of these datasets, and thus reducing the costs of such scans. Our paper has three main contributions over previous work. First, we present a novel and highly general method for sharing scans and thus amortizing their costs. This concept, which we call cyclic piggybacking, has a number of advantages over the more traditional batching scheme described in the literature. Second, we notice that the various subjobs generated in this manner can be assumed in an optimal schedule to respect a natural chain precedence ordering. Third, we describe a significant but natural generalization of the recently introduced FLEX scheduler for optimizing schedules within the context of this cyclic piggybacking paradigm, which can be tailored to a variety of cost metrics. Such cost metrics include average response time, average stretch, and any minimax-type metric—a total of 11 separate and standard metrics in all. Moreover, most of this carries over in the more general case of overlapping rather than identical datasets as well, employing what we will call semi-shared scans. In such scenarios, chain precedence is replaced by arbitrary precedence, but we can still handle 8 of the original 11 metrics. The overall approach, including both cyclic piggybacking and the FLEX scheduling generalization, is called CIRCUMFLEX. We describe some practical implementation strategies. And we evaluate the performance of CIRCUMFLEX via a variety of simulation and real benchmark experiments.     

A comparison of receiver-initiated and sender-initiated adaptive load sharing

Load sharing in a locally distributed system is the process of transparently distributing work submitted to the system by its users. By directing work away from nodes that are heavily loaded to nodes that are lightly loaded, system performance can be improved substantially.Adaptive load sharing policies make transfer decisions using information about the current system state. Control over the maintenance of this information and the initiation of load sharing actions may be centralized in a ‘server’ node or distributed among the system nodes participating in load sharing.The goal of this paper is to compare two strategies for adaptive load sharing with distributed control. In sender-initiated strategies, congested nodes search for lightly loaded nodes to which work may be transferred. In receiver-initiated strategies, the situation is reversed: lightly loaded nodes search for congested nodes from which work may be transferred. We show that sender-initiated strategies outperform receiver-initiated strategies at light to moderate system loads, and that receiver-initiated strategies are preferable at high system loads only if the costs of task transfer under the two strategies are comparable. (There are reasons to believe that the costs will be greater under receiver-initiated strategies, making sender-initiated strategies uniformly preferable.)     

On the Huffman and Alphabetic Tree Problem with General Cost Functions

We address generalized versions of the Huffman and Alphabetic Tree Problem where the cost caused by each individual leaf i, instead of being linear, depends on its depth in the tree by an arbitrary function. The objective is to minimize either the total cost or the maximum cost among all leaves. We review and extend the known results in this direction and devise a number of new algorithms and hardness proofs. It turns out that the Dynamic Programming approach for the Alphabetic Tree Problem can be extended to arbitrary cost functions, resulting in a time O(n ⁴) optimal algorithm using space O(n ³). We identify classes of cost functions where the well-known trick to reduce the runtime by a factor of n via a “monotonicity” property can be applied. For the generalized Huffman Tree Problem we show that even the k-ary version can be solved by a generalized version of the Coin Collector Algorithm of Larmore and Hirschberg (in Proc. SODA’90, pp. 310–318, 1990) when the cost functions are nondecreasing and convex. Furthermore, we give an O(n ²logn) algorithm for the worst case minimization variants of both the Huffman and Alphabetic Tree Problem with nondecreasing cost functions. Investigating the limits of computational tractability, we show that the Huffman Tree Problem in its full generality is inapproximable unless P = NP, no matter if the objective function is the sum of leaf costs or their maximum. The alphabetic version becomes NP-hard when the leaf costs are interdependent.     

Boosted multi-task learning

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? ₁-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.     

Randomized control of T-policy for an M/G/1 system

We study an M/G/1 queueing system with a server that can be switched on and off. The server can take a vacation time T after the system becomes empty. In this paper, we investigate a randomized policy to control a server with which, when the system is empty, the server can be switched off with probability p and take a vacation or left on with probability (1 − p) and continue to serve the arriving customers. For this system, we consider the operating cost and the holding cost where the operating cost consists of the system running and switching costs (start up and shut down costs). We describe the structure and characteristics of this policy and solve a constrained problem to minimize the average operating cost per unit time under the constraint for the holding cost per unit time.     

Private aggregation for presence streams

Collaboration technologies must support information sharing between collaborators, but must also take care not to share too much information or share information too widely. Systems that share information without requiring an explicit action by a user to initiate the sharing must be particularly cautious in this respect. Presence systems are an emerging class of applications that support collaboration. Through the use of pervasive sensors, these systems estimate user location, activities, and available communication channels. Because such presence data are sensitive, to achieve wide-spread adoption, sharing models must reflect the privacy and sharing preferences of their users. This paper looks at the role that privacy-preserving aggregation can play in addressing certain user sharing and privacy concerns with respect to presence data.We define conditions to achieve CollaPSE (Collaboration Presence Sharing Encryption) security, in which (i) an individual has full access to her own data, (ii) a third party performs computation on the data without learning anything about the data values, and (iii) people with special privileges called “analysts” can learn statistical information about groups of individuals, but nothing about the individual values contributing to the statistic other than what can be deduced from the statistic. More specifically, analysts can decrypt aggregates without being able to decrypt the individual values contributing to the aggregate. Based in part on studies we carried out that illustrate the need for the conditions encapsulated by CollaPSE security, we designed and implemented a family of CollaPSE protocols. We analyze their security, discuss efficiency tradeoffs, describe extensions, and review more recent privacy-preserving aggregation work.     

Catching a viral video

The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos—videos that become popular through internet sharing. In this paper we seek to better understand viral videos on YouTube by analyzing sharing and its relationship to video popularity using millions of YouTube videos. The socialness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link, Facebook referral) or non-social (e.g. a link from related videos). We find that viewership patterns of highly social videos are very different from less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. By using our insights on viral videos we are able develop a method for ranking blogs and websites on their ability to spread viral videos.     

Modelling and performance analysis of content sharing and distribution in community networks with infrastructure support

We consider the problem of sharing a viral file between users in a local community network (e.g., college and office campuses). Community computing is computer networking among and between users in a geographically bounded setting for local purposes and activities. Due to the community-oriented nature of such networks, it is likely that users of a community network would like to share content. Peer-to-Peer (P2P) networks have turned out to be one of the most innovative paradigms for sharing content on the Internet. In this paper, we analyze the performance of P2P content sharing in community networks and investigate the role that infrastructure nodes (helpers) can play to enhance the performance of content sharing and distribution. We model the evolution of content demand in a community network. The use of this demand prediction model allows us to design a delicate P2P-with-helpers content distribution system. Our insights that we obtain using fluid-flow model increase our understanding of how helper provisioning affects the performance of content sharing and distribution. The derived results show that significant reduction in both the cost of distributing content and the average content download time can be realized when only few infrastructure nodes in the community network play the role helpers and cache P2P objects.     

Group-oriented setting’s multisigncryption scheme with threshold designcryption

Signcryption supports the authenticity and confidently in a single logic step and it has more efficient cost than the signature-then-encryption approach. Currently signcryption schemes only support a peer to peer secure message transfer between single users, and thus it cannot be adapted to the group users in multiple senders and receivers. In this paper, we propose a group-oriented multisigncryption scheme with threshold designcryption in multi-users setting, which combines the threshold secret sharing technology, zero knowledge proof for the bilinear map equality of two discrete logarithms, multi-signature and identity-based signcryption schemes. The ciphertext in the proposed scheme is computed by all the m members in sender group, and it can be decrypted only by t-out-of-n members in receiver group. We prove the security of the proposed scheme in several models including semantic security, unforgeability, public ciphertext authenticity and verifiability.     

Reflection-through-performance: personal implications of documenting health behaviors for the collective

Previous work has examined how technology can support health behavior monitoring in social contexts. These tools incentivize behavior documentation through the promise of virtual rewards, rich visualizations, and improved co-management of disease. Social influence is leveraged to motivate improved behaviors through friendly competition and the sharing of emotional and informational support. Prior work has described how by documenting and sharing behaviors in these tools, people engage in performances of the self. This performance happens as users selectively determine what information to share and hide, crafting a particular portrayal of their identity. Much of the prior work in this area has examined the implications of systems that encourage people to share their behaviors with friends, family, and geographically distributed strangers. In this paper, we report upon the performative nature of behavior sharing in a system created for a different social group: the local neighborhood. We designed Community Mosaic (CM), a system with a collectivistic focus: this tool asks users to document their behaviors using photographs and text, but not for their own benefit—for the benefit of others in their community. Through a 6-week deployment of CM, we evaluated the nature of behavior sharing in this system, including participants’ motivations for sharing, the way in which this sharing happened, and the reflexive impact of sharing. Our findings highlight the performative aspects of photograph staging and textual narration and how sharing this content led participants to become more aware and evaluative of their behaviors, and led them to try to eat more healthfully. We conclude with recommendations for behavior monitoring tools, specifically examining the implications of users’ perceived audience and automated behavioral tracking on opportunities for reflection-through-performance.     

Mosco: a privacy-aware middleware for mobile social computing

The proliferation of mobile devices coupled with Internet access is generating a tremendous amount of highly personal and sensitive data. Applications such as location-based services and quantified self harness such data to bring meaningful context to users’ behavior. As social applications are becoming prevalent, there is a trend for users to share their mobile data. The nature of online social networking poses new challenges for controlling access to private data, as compared to traditional enterprise systems. First, the user may have a large number of friends, each associated with a unique access policy. Second, the access control policies must be dynamic and fine-grained, i.e. they are content-based, as opposed to all-or-nothing. In this paper, we investigate the challenges in sharing of mobile data in social applications. We design and evaluate a middleware running on Google App Engine, named Mosco, that manages and facilitates sharing of mobile data in a privacy-preserving manner. We use Mosco to develop a location sharing and a health monitoring application. Mosco helps shorten the development process. Finally, we perform benchmarking experiments with Mosco, the results of which indicate small overhead and high scalability.