Decentralized reputation

Reputation systems constitute one of the few workable mechanisms for distributed applications in which users can be made accountable for their actions. By collecting user experiences in reputation profiles, participants are encouraged to interact more with well-behaving peers hence better online behavior is motivated. In this work, we develop a privacy-preserving reputation scheme for collaborative systems such as P2P networks in which peers can represent themselves with different pseudonyms when interacting with others. All these pseudonyms, however, are bound to the same reputation token, allowing honest peers to maintain their good record, even when switching to a new pseudonym, while at the same time preventing malicious peers from making a fresh start. Our system is truly decentralized. Using an append-only distributed ledger such as Bitcoin’s blockchain, we show how participants can make anonymous yet verifiable assertions about their own reputation. In particular, reputation can be demonstrated and updated effectively using efficient zkSNARK proofs. The system maintains soundness, peer-pseudonym unlinkability as well as unlinkability among pseudonyms of the same peer. We formally prove these properties and we evaluate the efficiency of the various operations envisioned in our scheme

Privacy-respecting Reward Generation and Accumulation for Participatory Sensing Applications

Participatory or crowd-sensing applications process sensory data contributed by users and transform them to simple visualizations (such as for example noise or pollution levels) that help create an accurate representation of the surrounding environment. Although contributed data is of great interest to individuals, the involvement of citizens and community groups, however, is still limited. Hence, incentivizing users to increase participation seems crucial for the success of participatory sensing. In this paper, we develop a privacy-preserving rewarding scheme which allows campaign administrators to reward users for the data they contribute. Our system of anonymous tokens allow users to enjoy the benefits of participation while at the same time ensuring their anonymity. Moreover, rewards can be accumulated together thus further increasing the level of privacy offered by the system. Our proposal is coupled with a security analysis showing the privacy-preserving character of the system along with an efficiency analysis demonstrating the feasibility of our approach in realistic deployment settings

From Zebras to Tigers: Incentivizing participation in Crowd-sensing applications through fair and private Bitcoin rewards

In this work we develop a rewarding framework that can be used as a building block in crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. Thus monetary incentives could help attract a large number of participants, thereby increasing not only the amount but also the quality of sensed data. Our first contribution in this work is to ensure that users can submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, we thwart malicious user behavior such as double-redeeming attempts where a user tries to obtain rewards for multiple submissions of the same data. More importantly, we ensure the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, we eliminate the trust placed on third parties in traditional fair exchange protocols. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, we only rely on simple Bitcoin transactions as opposed to prior works that are based on the use of highly complex smart contracts

Constant Factor Approximation for Balanced Cut in the PIE model

We propose and study a new semi-random semi-adversarial model for Balanced Cut, a planted model with permutation-invariant random edges (PIE). Our model is much more general than planted models considered previously. Consider a set of vertices V partitioned into two clusters $L$ and $R$ of equal size. Let $G$ be an arbitrary graph on $V$ with no edges between $L$ and $R$. Let $E_{random}$ be a set of edges sampled from an arbitrary permutation-invariant distribution (a distribution that is invariant under permutation of vertices in $L$ and in $R$). Then we say that $G + E_{random}$ is a graph with permutation-invariant random edges. We present an approximation algorithm for the Balanced Cut problem that finds a balanced cut of cost $O(|E_{random}|) + n \text{polylog}(n)$ in this model. In the regime when $|E_{random}| = \Omega(n \text{polylog}(n))$, this is a constant factor approximation with respect to the cost of the planted cut.Comment: Full version of the paper at the 46th ACM Symposium on the Theory of Computing (STOC 2014). 32 page

CENSOR: Privacy-preserving Obfuscation for Outsourcing SAT formulas

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT. In this work, we develop CENSOR (privaCy prEserviNg obfuScation for Outsourcing foRmulas), a novel SAT obfuscation framework that resembles Indistinguishability Obfuscation. At the core of the framework lies a mechanism that transforms any formula to a random one with the same number of satisfying assignments. As a result, obfuscated formulas are indistinguishable from each other thus preserving the input-output privacy of the original SAT instance. Contrary to prior solutions that are rather adhoc in nature, we formally prove the security of our scheme. Additionally, we show that obfuscated formulas are within a polynomial factor of the original ones thus achieving polynomial slowdown. Finally, the whole process is efficient in practice, allowing solutions to original instances to be easily recovered from obfuscated ones. A byproduct of our method is that all NP problems can be potentially outsourced to the cloud by means of reducing to SAT

Incentivizing Participation in Crowd-Sensing Applications Through Fair and Private Bitcoin Rewards

In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate because sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.publishedVersionPeer reviewe

Private Lives Matter: A Differential Private Functional Encryption Scheme (extended version)

The use of data combined with tailored statistical analysis have presented a unique opportunity to organizations in diverse fields to observe users\u27 behaviors and needs, and accordingly adapt and fine-tune their services. However, in order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. The use of statistical databases for releasing data analytics is growing exponentially, and while many cryptographic methods are utilized to protect the confidentiality of the data -- a task that has been ably carried out by many authors over the years -- only a few %rudimentary number of works focus on the problem of privatizing the actual databases. Believing that securing and privatizing databases are two equilateral problems, in this paper, we propose a hybrid approach by combining Functional Encryption with the principles of Differential Privacy. Our main goal is not only to design a scheme for processing statistical data and releasing statistics in a privacy-preserving way but also to provide a richer, more balanced, and comprehensive approach in which data analytics and cryptography go hand in hand with a shift towards increased privacy

Multi-party trust computation in decentralized environments in the presence of malicious adversaries

In this paper, we describe a decentralized privacy-preserving protocol for securely casting trust ratings in distributed reputation systems. Our protocol allows n participants to cast their votes in a way that preserves the privacy of individual values against both internal and external attacks. The protocol is coupled with an extensive theoretical analysis in which we formally prove that our protocol is resistant to collusion against as many as n-1 corrupted nodes in both the semi-honest and malicious adversarial models. The behavior of our protocol is tested in a real P2P network by measuring its communication delay and processing overhead. The experimental results uncover the advantages of our protocol over previous works in the area; without sacrificing security, our decentralized protocol is shown to be almost one order of magnitude faster than the previous best protocol for providing anonymous feedback

SAT Distributions with Planted Assignments and Phase Transitions between Decision and Optimization Problems

We present a generator for weighted instances of MAX k-SAT in which every clause has a weight associated with it and the goal is to maximize the total weight of satisfied clauses. Our generator produces formulas whose hardness can be finely tuned by two parameters p and δ that control the weights of the clauses. Under the right choice of these parameters an easy-hard-easy pattern in the search complexity emerges which is similar to the patterns observed for traditional SAT distributions. What is remarkable, however, is that the generated distributions seem to lie in the middle ground between decision and optimization problems. Increasing the value of p from 0 to 1 has the effect of changing the shape of the computational cost from an easy-hard-easy pattern which is typical of decision problems to an easy-hard pattern which is typical of optimization problems. Thus our distributions seem to bridge the gap between decision and optimization versions of SAT. Furthermore, we demonstrate that these phase transitions are related to sudden changes to a quantity similar to the backbone of a SAT formula. In our model not only we know how the optimal solution looks like (because we plant it in advance) but we also give evidence that it is unique. Thus our generator comes with an indication of optimality of the planted assignment which is basically the structural property that is related to the phase transition phenomena observed