text: We introduce DaiMoN, a decentralized artificial intelligence model network, which incentivizes peer collaboration in improving the accuracy of machine learning models for a given classification problem. It is an autonomous network where peers may submit models with improved accuracy and other peers may verify the accuracy improvement. The system maintains an append-only decentralized ledger to keep the log of critical information, including who has trained the model and improved its accuracy, when it has been improved, by how much it has improved, and where to find the newly updated model. DaiMoN rewards these contributing peers with cryptographic tokens. A main feature of DaiMoN is that it allows peers to verify the accuracy improvement of submitted models without knowing the test labels. This is an essential component in order to mitigate intentional model overfitting by model-improving peers. To enable this model accuracy evaluation with hidden test labels, DaiMoN uses a novel learnable Distance Embedding for Labels (DEL) function proposed in this paper. Specific to each test dataset, DEL scrambles the test label vector by embedding it in a low-dimension space while approximately preserving the distance between the dataset's test label vector and a label vector inferred by the classifier. It therefore allows proof-of-improvement (PoI) by peers without providing them access to true test labels. We provide analysis and empirical evidence that under DEL, peers can accurately assess model accuracy. We also argue that it is hard to invert the embedding function and thus, DEL is resilient against attacks aiming to recover test labels in order to cheat. Our prototype implementation of DaiMoN is available at this https URL.
  @cite_27: We consider localitg-preserving hashing — in which adjacent points in the domain are mapped to adjacent or nearlyadjacent points in the range — when the domain is a ddimensional cube. This problem has applications to highdimensional search and multimedia indexing. We show that simple and natural classes of hash functions are provably good for this problem. We complement this with lower bounds suggesting that our results are essentially the best possible.
  @cite_29: We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living in R d , the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such as the approximate minimum spanning tree. The article is based on the material from the authors' STOC'98 and FOCS'01 papers. It unifies, generalizes and simplifies the results from those papers.
  @cite_32: (MATH) A locality sensitive hashing scheme is a distribution on a family @math of hash functions operating on a collection of objects, such that for two objects x,y, PrheF[h(x) = h(y)] = sim(x,y), where sim(x,y) e [0,1] is some similarity function defined on the collection of objects. Such a scheme leads to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality sensitive hashing scheme for a collection of subsets with the set similarity measure sim(A,B) = |A P B| |A P Ehe [d(h(P),h(Q))] x O(log n log log n). EMD(P, Q). .
  @cite_8: 
summary: One area of related work is on data-independent locality sensitive hashing (LSH) @cite_29 and data-dependent locality preserving hashing (LPH) @cite_27 @cite_8 . LSH hashes input vectors so that similar vectors have the same hash value with high probability. There are many algorithms in the family of LSH. One of the most common LSH methods is the random projection method called SimHash @cite_32 , which uses a random hyperplane to hash input vectors.