Python - NCU

Python - NCU

Technical Report CS-2009-22, 2009 University of Waterloo Presented by Jehn-Ruey Jiang Users Obedient users: abide by protocol rules v.s. Rational users: attempt to maximize their consumption of system resources while

minimizing the use of their own Free Rider Users who try to benefit from a system without making any contribution to the system About 75% of users are free-riders free-riders

Common Incentive Mechanisms Currency: In MojoNation, peers earn currency by making contributions and use the earned currency to purchase service from other peers. Reputation: In KaZaA, peers increase their reputation by uploading and later use their high reputation scores during downloading.

Free Riding Related Problems White Washing Attack: a free-rider repeatedly rejoins the network under new identities to avoid the penalty imposed on free-riders. The whitewashing attack is made feasible by the availability of low cost identities or cheap pseudonyms. Two ways to counter the attack:

To assign strong free IDs by a centrally trusted authority To impose penalties on all newcomers Free Riding Related Problems Sybil Attack: a single malicious identity can present multiple identities, and thus gain control over part of the network. (An attacker thwarts the reputation and sharing mechanism of a P2P network by creating a large number of identities or pseudonyms, using

them to gain a disproportionately large influence.) Michael Piatek, Tomas Isdal, Thomas Anderson, Arvind Krishnamurthy, and Arun Venkataramani. Do incentives build robustness in bittorrent? In NSDI07, Cambridge, MA, April 2007. Contribution? The paper seeks to show a strategic peer can

raise download speeds while still contributing the same Already proven by BitThief (Free Riding in BiTorrent is Cheap, Locher et al., HotNets, 2006) high download rates without uploading any data Strategic? Benefit of optimistic unchoking: Might

discover faster peers at all points in time BitTyrant abolishes optimistic unchoking; it is designed not to upload too much if you cannot download so much in return Instead, look for people with higher upload rates instead so you can do a better deal Benefits people with higher upload capacities Building BitTyrant: A strategic client

Un-choking Algorithm dp download rate of connection p up upload rate of connection p 11 Building BitTyrant: A strategic client How to improve performance Maximize reciprocation bandwidth per connection

Maximize number of reciprocating peers Deviate from equal split From previous result (from equal split to unequal split) 100 KB/s 15 KB/s, probability of reciprocation1% 15 KB/s 10 KB/s, probability of reciprocation40% 12

Building BitTyrant: A strategic client Un-choking Algorithm = 10% = 20% r=3 13

Virtual Currency KARMA [25] proposes a general economic framework of virtual currency for combating free-riders in p2p systems by keeping track of resource contribution and resource consumption of each member of the system. This is achieved by representing the overall performance of each participant via a single metric called karma.

Banking The karma values for each node are maintained by a set of other nodes (called the bank-set) who are collectively responsible for continuously increasing and decreasing the karma value for that node as it contributes and consumes resources consumption of each member of the system. Initially a user is awarded a seed amount of karma

when s/he joins the system (this can encourage white washing). Bank-set Mapping The KARMA design assumes that there are at least k available nodes in the system at all time instances and that a certain fraction of these nodes are non-malicious. The bankset information is maintained via a

Distributed Hash Table (DHT) where each node is mapped to bank-sets. The k closest nodes in the identifier space of each node A constitute the bank-set for A. Selecting peers for downloading KARMA maintains file information using a fileID for each file. When a node joins, it associates its id with the fileIDs of all files that

it possesses. A node willing to download a file acquires a list of potential up-loaders to select the peer to download from. Karma Exchange The karma transfer from A to B is initiated when A sends B a signed message authorizing BankA to transfer a given amount of karma to

B, which forwards this message to BankB which in turn contacts BankA. If A has sufficient karma in its account, the amount is deducted from As account and credited to Bs account. Drawback Peers are required to act as bankers for other nodes; no peer has any incentive to take this

additional responsibility. Karma may introduce additional free-riding behavior in an attempt to cope with freeriding itself! ==> the new free-riding problem Credence A P2P distributed reputation system, designed to thwart content pollution in P2P file sharing systems. File Pollution in P2P networks

Wasted resources Mislabeled content Mal-content (viruses and Trojans) A fully functioning Credence is build as an extension to the LimeWire client for the Gnutella file sharing network. Overview of the approach

Goal: To distinguish between authentic content and polluted content 1. Users VOTE on objects based on their judgment 2. Users COLLECT votes to evaluate authenticity of the object they are querying 3. Users EVALUATE votes from peers to determine

credibility of peers from their perspective Search Results Each search result can be viewed as a claim about the file's attributes. For example, makes the claim that the file with content hash H has the specified attributes. The symbol is used to indicate that

gettysburg is one of possibly many valid names for the file. Votes in Credence A Vote is a signed tuple: K

H - File content hash S Statement about the file T Timestamp

K Certificate Votes are cryptographically signed to ensure non-repudiation and to prevent sybil attacks Statement: For example, Collecting and Storing Votes Vote-gather query: to use the existing query

infrastructure by issuing a vote-gather query, specifying the hash of the file of interest, to the underlying Gnutella network. Reactive, pull-based Highest weight votes sent if multiple votes stored by a given peer Vote-database:

votes received> Computing Correlations Compare shared voting history for each pair of peers Correlation coefficient obtained by comparing conflicting votes and agreeing votes takes a range of [-1,1] Positive values indicate agreement Negative values indicate disagreement

Responses to Attacks Nave attacker ( votes consistently in opposition to honest clients will be cut off) Random attacker ( will generate no correlations with other peers Rational attacker (some files with honest votes, dishonest on others has to leak some correct information in the network) Whitewasher attacker first votes honestly on a large set of files before voting dishonestly on a smaller TARGET set. Clients own

perspective at evaluating a peer comes into play here to exclude the attacker. Q&A

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